good morning all you guys have heard of the stories are on Facebook
the stories are on Facebook
the stories are on Facebook stop certain parts talking to each other
stop certain parts talking to each other
stop certain parts talking to each other because they invented their own language
because they invented their own language
because they invented their own language you’ve heard of Tesla creating cars
you’ve heard of Tesla creating cars
you’ve heard of Tesla creating cars which are which between predict crashes
which are which between predict crashes
which are which between predict crashes which drive on their own yeah ladies and
which drive on their own yeah ladies and
which drive on their own yeah ladies and gentlemen welcome to the world of
gentlemen welcome to the world of
gentlemen welcome to the world of artificial intelligence this is what is
artificial intelligence this is what is
artificial intelligence this is what is it all about
it all about
it all about this is computers talking to us
this is computers talking to us
this is computers talking to us computers doing things for us computers
computers doing things for us computers
computers doing things for us computers thinking for us that’s that’s the world
thinking for us that’s that’s the world
thinking for us that’s that’s the world of artificial intelligence now before we
of artificial intelligence now before we
of artificial intelligence now before we talk about artificial intelligence it’s
talk about artificial intelligence it’s
talk about artificial intelligence it’s important to understand what does what
important to understand what does what
important to understand what does what is intelligence what does general
is intelligence what does general
is intelligence what does general intelligence mean yeah so what what does
intelligence mean yeah so what what does
intelligence mean yeah so what what does intelligence mean I think this is
intelligence mean I think this is
intelligence mean I think this is probably not very legible
probably not very legible
probably not very legible I’ll anyway talk through to talk through
I’ll anyway talk through to talk through
I’ll anyway talk through to talk through those things so there are various models
those things so there are various models
those things so there are various models of intelligence that have been created
of intelligence that have been created
of intelligence that have been created since since time immemorial the one you
since since time immemorial the one you
since since time immemorial the one you see was created by somebody called as
see was created by somebody called as
see was created by somebody called as Howard Gardner if you talk to various
Howard Gardner if you talk to various
Howard Gardner if you talk to various people they have different views about
people they have different views about
people they have different views about it if you if you if you’ve heard is tine
it if you if you if you’ve heard is tine
it if you if you if you’ve heard is tine Einstein used to say that intelligence
Einstein used to say that intelligence
Einstein used to say that intelligence is all about you know ability to think
is all about you know ability to think
is all about you know ability to think imagination and so forth if you talk to
imagination and so forth if you talk to
imagination and so forth if you talk to somebody like a wren a descarte Wren a
somebody like a wren a descarte Wren a
somebody like a wren a descarte Wren a discarded given the famous dictum cogito
discarded given the famous dictum cogito
discarded given the famous dictum cogito ergo sum which means I think therefore I
ergo sum which means I think therefore I
ergo sum which means I think therefore I am
am
am so what exactly is intelligence if you
so what exactly is intelligence if you
so what exactly is intelligence if you look at the dictionary the definition is
look at the dictionary the definition is
look at the dictionary the definition is quite small and and you know sweet it
quite small and and you know sweet it
quite small and and you know sweet it says intelligences our ability to apply
says intelligences our ability to apply
says intelligences our ability to apply knowledge and skills but is that really
knowledge and skills but is that really
knowledge and skills but is that really intelligence this model that you see
intelligence this model that you see
intelligence this model that you see here it gives you various flavors of
here it gives you various flavors of
here it gives you various flavors of intelligence so for example somebody
intelligence so for example somebody
intelligence so for example somebody who’s a good mathematician is his mind
who’s a good mathematician is his mind
who’s a good mathematician is his mind thinks on a logical side of things
thinks on a logical side of things
thinks on a logical side of things somebody who has who knows multiple
somebody who has who knows multiple
somebody who has who knows multiple languages very different from
languages very different from
languages very different from mathematics right somebody who is very
mathematics right somebody who is very
mathematics right somebody who is very good at developing interpersonal and
good at developing interpersonal and
good at developing interpersonal and intrapersonal skills that’s a different
intrapersonal skills that’s a different
intrapersonal skills that’s a different type of intelligence that’s required
type of intelligence that’s required
type of intelligence that’s required right somebody with who’s a good signal
right somebody with who’s a good signal
right somebody with who’s a good signal or or somebody who’s a good sportsman
or or somebody who’s a good sportsman
or or somebody who’s a good sportsman now what exactly is intelligence because
now what exactly is intelligence because
now what exactly is intelligence because these are absolutely different and
these are absolutely different and
these are absolutely different and almost orthogonal facets of intelligence
almost orthogonal facets of intelligence
almost orthogonal facets of intelligence so what is intelligence the bad news is
so what is intelligence the bad news is
so what is intelligence the bad news is that nobody seems to agree what what
that nobody seems to agree what what
that nobody seems to agree what what what is one single way of measuring
what is one single way of measuring
what is one single way of measuring intelligence the good news is that by
intelligence the good news is that by
intelligence the good news is that by now there is a general agreement that
now there is a general agreement that
now there is a general agreement that we’ll all of these constitutes towards
we’ll all of these constitutes towards
we’ll all of these constitutes towards multiple
multiple
multiple forms of intelligence the reason I took
forms of intelligence the reason I took
forms of intelligence the reason I took this detour is for furry fur for all of
this detour is for furry fur for all of
this detour is for furry fur for all of us to understand that when we talk of
us to understand that when we talk of
us to understand that when we talk of artificial intelligence we need to
artificial intelligence we need to
artificial intelligence we need to understand what intelligence is and more
understand what intelligence is and more
understand what intelligence is and more importantly we need to understand that
importantly we need to understand that
importantly we need to understand that there is no single definition of
there is no single definition of
there is no single definition of intelligence to start with moving on so
intelligence to start with moving on so
intelligence to start with moving on so before I get into the world of AI and
before I get into the world of AI and
before I get into the world of AI and and and and and and so forth let me let
and and and and and so forth let me let
and and and and and so forth let me let me let me take you back and how we’ve
me let me take you back and how we’ve
me let me take you back and how we’ve been using machines one of the primary
been using machines one of the primary
been using machines one of the primary uses of machines that we’ve developed
uses of machines that we’ve developed
uses of machines that we’ve developed over the years is around automating our
over the years is around automating our
over the years is around automating our work right from the time of stone age
work right from the time of stone age
work right from the time of stone age where we invented tools till till the
where we invented tools till till the
where we invented tools till till the current times we are actually the whole
current times we are actually the whole
current times we are actually the whole idea behind machines is to automate now
idea behind machines is to automate now
idea behind machines is to automate now how is artificial intelligence doing it
how is artificial intelligence doing it
how is artificial intelligence doing it differently it’s important to understand
differently it’s important to understand
differently it’s important to understand this cars for example did automation for
this cars for example did automation for
this cars for example did automation for transportation yeah there were machines
transportation yeah there were machines
transportation yeah there were machines in the Industrial Revolution which
in the Industrial Revolution which
in the Industrial Revolution which automated manufacturing to a large
automated manufacturing to a large
automated manufacturing to a large extent so how is AI different from that
extent so how is AI different from that
extent so how is AI different from that Automation a small point but it’s very
Automation a small point but it’s very
Automation a small point but it’s very important to understand the big
important to understand the big
important to understand the big difference between now and then is that
difference between now and then is that
difference between now and then is that now we have developed an ability in the
now we have developed an ability in the
now we have developed an ability in the machines to actually start learning let
machines to actually start learning let
machines to actually start learning let me give you a small example all of you
me give you a small example all of you
me give you a small example all of you have seen a barcode reader in in in
have seen a barcode reader in in in
have seen a barcode reader in in in retail shops now this barcode reader is
retail shops now this barcode reader is
retail shops now this barcode reader is pretty dumb normally what you see in the
pretty dumb normally what you see in the
pretty dumb normally what you see in the retail shops it’s pretty dumb but the
retail shops it’s pretty dumb but the
retail shops it’s pretty dumb but the the the the thing is that the barcode
the the the thing is that the barcode
the the the thing is that the barcode reader tries to read the barcode and
reader tries to read the barcode and
reader tries to read the barcode and such as its number but if you scratch
such as its number but if you scratch
such as its number but if you scratch the barcode the paper where the barcode
the barcode the paper where the barcode
the barcode the paper where the barcode is printed it’s not able to recognize it
is printed it’s not able to recognize it
is printed it’s not able to recognize it now there is an artificially intelligent
now there is an artificially intelligent
now there is an artificially intelligent version of it what happens is now and
version of it what happens is now and
version of it what happens is now and it’s also after the hardware is still
it’s also after the hardware is still
it’s also after the hardware is still the same what happens effectively is
the same what happens effectively is
the same what happens effectively is that this program for the first time it
that this program for the first time it
that this program for the first time it sees a scratch it is still not able to
sees a scratch it is still not able to
sees a scratch it is still not able to recognize it so it behaves no different
recognize it so it behaves no different
recognize it so it behaves no different from the dumb version of dumbed down
from the dumb version of dumbed down
from the dumb version of dumbed down version of artificial intelligence
version of artificial intelligence
version of artificial intelligence though of the barcode reader but ah
though of the barcode reader but ah
though of the barcode reader but ah so what what that means is the human
so what what that means is the human
so what what that means is the human intervenes and human enters the number
intervenes and human enters the number
intervenes and human enters the number but more importantly the barcode reader
but more importantly the barcode reader
but more importantly the barcode reader or the computer program that is running
or the computer program that is running
or the computer program that is running the barcode reader learns from that so
the barcode reader learns from that so
the barcode reader learns from that so after a couple of interactions the
after a couple of interactions the
after a couple of interactions the barcode reader is able to understand
barcode reader is able to understand
barcode reader is able to understand scratches and you know certain
scratches and you know certain
scratches and you know certain discontinuities in the barcode and it
discontinuities in the barcode and it
discontinuities in the barcode and it starts recognizing images
starts recognizing images
starts recognizing images so that is the big difference between
so that is the big difference between
so that is the big difference between what was going on earlier and now now we
what was going on earlier and now now we
what was going on earlier and now now we have an ability to train the machines to
have an ability to train the machines to
have an ability to train the machines to actually learn we don’t have to put all
actually learn we don’t have to put all
actually learn we don’t have to put all the data upfront all we have to do is
the data upfront all we have to do is
the data upfront all we have to do is give it certain set of conditions and it
give it certain set of conditions and it
give it certain set of conditions and it is able to generalize those conditions
is able to generalize those conditions
is able to generalize those conditions over a period of time so that’s the
over a period of time so that’s the
over a period of time so that’s the important difference between what was
important difference between what was
important difference between what was happening earlier and what where a is
happening earlier and what where a is
happening earlier and what where a is taking us to so what are the how did we
taking us to so what are the how did we
taking us to so what are the how did we get here it’s important to understand
get here it’s important to understand
get here it’s important to understand that you know I’ll start with the 1950s
that you know I’ll start with the 1950s
that you know I’ll start with the 1950s because the concept of automatons has
because the concept of automatons has
because the concept of automatons has been there forever even in mythology so
been there forever even in mythology so
been there forever even in mythology so I’ll not go there but the important
I’ll not go there but the important
I’ll not go there but the important timelines that you see on the slide
timelines that you see on the slide
timelines that you see on the slide there those important events are good to
there those important events are good to
there those important events are good to know first first and foremost was the
know first first and foremost was the
know first first and foremost was the okay yeah and back to the slide so the
okay yeah and back to the slide so the
okay yeah and back to the slide so the first and foremost was the concept of
first and foremost was the concept of
first and foremost was the concept of what is called as a curing test the
what is called as a curing test the
what is called as a curing test the Turing test was Sir Alan Turing is
Turing test was Sir Alan Turing is
Turing test was Sir Alan Turing is considered to be modern of to be your
considered to be modern of to be your
considered to be modern of to be your father of modern computers and what
father of modern computers and what
father of modern computers and what during actually created was a test of
during actually created was a test of
during actually created was a test of how do you define something is a
how do you define something is a
how do you define something is a computer or a human and what the Turing
computer or a human and what the Turing
computer or a human and what the Turing test says is that if a human interacts
test says is that if a human interacts
test says is that if a human interacts with the computer and and he doesn’t
with the computer and and he doesn’t
with the computer and and he doesn’t know that he’s interacting with a
know that he’s interacting with a
know that he’s interacting with a computer or a human and if he’s not able
computer or a human and if he’s not able
computer or a human and if he’s not able to recognize a computer and a human then
to recognize a computer and a human then
to recognize a computer and a human then the computer is considered to be
the computer is considered to be
the computer is considered to be intelligent that’s the basic idea of
intelligent that’s the basic idea of
intelligent that’s the basic idea of Turing test
Turing test
Turing test now that was created the theoretical
now that was created the theoretical
now that was created the theoretical model of what what is intelligence in
model of what what is intelligence in
model of what what is intelligence in the respect to computers was created way
the respect to computers was created way
the respect to computers was created way back in 1950 and the entire mathematics
back in 1950 and the entire mathematics
back in 1950 and the entire mathematics behind it because lot of these fancy
behind it because lot of these fancy
behind it because lot of these fancy things which I showed you on for example
things which I showed you on for example
things which I showed you on for example on the in the video clip they are all
on the in the video clip they are all
on the in the video clip they are all part of you know the the basis behind it
part of you know the the basis behind it
part of you know the the basis behind it is a lot of mathematics and probability
is a lot of mathematics and probability
is a lot of mathematics and probability theory and so forth I’ll not go into
theory and so forth I’ll not go into
theory and so forth I’ll not go into those details but it’s important to
those details but it’s important to
those details but it’s important to understand that the work was done in the
understand that the work was done in the
understand that the work was done in the 60s and DARPA which is the u.s. Pentagon
60s and DARPA which is the u.s. Pentagon
60s and DARPA which is the u.s. Pentagon funded agency and and the US or the UK
funded agency and and the US or the UK
funded agency and and the US or the UK Department of Defense they funded a lot
Department of Defense they funded a lot
Department of Defense they funded a lot of programs on artificial intelligence
of programs on artificial intelligence
of programs on artificial intelligence especially around what is called as
especially around what is called as
especially around what is called as artificial general intelligence which is
artificial general intelligence which is
artificial general intelligence which is a computer is able to show emotions a
a computer is able to show emotions a
a computer is able to show emotions a computer is able to learn a computer is
computer is able to learn a computer is
computer is able to learn a computer is able to you know think through general
able to you know think through general
able to you know think through general conditions rather than feeding
conditions rather than feeding
conditions rather than feeding everything up front those are the sort
everything up front those are the sort
everything up front those are the sort of things that happen however towards
of things that happen however towards
of things that happen however towards the end of 60s what people realize what
the end of 60s what people realize what
the end of 60s what people realize what it was too hard a problem to solve they
it was too hard a problem to solve they
it was too hard a problem to solve they just didn’t have the
just didn’t have the
just didn’t have the building power or other things to take
building power or other things to take
building power or other things to take it further so what what happened in the
it further so what what happened in the
it further so what what happened in the 70s was what is classically called as
70s was what is classically called as
70s was what is classically called as the AI winter all the ideas around
the AI winter all the ideas around
the AI winter all the ideas around artificial general intelligence were
artificial general intelligence were
artificial general intelligence were dropped down and what people started
dropped down and what people started
dropped down and what people started doing was basically around artificial
doing was basically around artificial
doing was basically around artificial applied artificial intelligence so just
applied artificial intelligence so just
applied artificial intelligence so just solve small problems which are practical
solve small problems which are practical
solve small problems which are practical and which make you money however around
and which make you money however around
and which make you money however around 1980s for example the the interest was
1980s for example the the interest was
1980s for example the the interest was revived again there was a concept called
revived again there was a concept called
revived again there was a concept called as expert systems which was basically
as expert systems which was basically
as expert systems which was basically systems which were fed enough data and
systems which were fed enough data and
systems which were fed enough data and they were able to give intelligent
they were able to give intelligent
they were able to give intelligent answers and the revival happened again
answers and the revival happened again
answers and the revival happened again however in the 1990s I think the big
however in the 1990s I think the big
however in the 1990s I think the big event was if those of you remember Garry
event was if those of you remember Garry
event was if those of you remember Garry Kasparov the reigning chess champion who
Kasparov the reigning chess champion who
Kasparov the reigning chess champion who was unbeaten was actually beaten by the
was unbeaten was actually beaten by the
was unbeaten was actually beaten by the IBM computer so that was a big event
IBM computer so that was a big event
IBM computer so that was a big event which which created a lot of hype around
which which created a lot of hype around
which which created a lot of hype around computers becoming intelligent however
computers becoming intelligent however
computers becoming intelligent however things really started moving in the
things really started moving in the
things really started moving in the 2000s so the first event on that was
2000s so the first event on that was
2000s so the first event on that was Google creating self-driving cars now
Google creating self-driving cars now
Google creating self-driving cars now these self-driving cars created the
these self-driving cars created the
these self-driving cars created the entire framework around which further
entire framework around which further
entire framework around which further research happened especially the
research happened especially the
research happened especially the mathematical framework and the computing
mathematical framework and the computing
mathematical framework and the computing framework the other interesting bit
framework the other interesting bit
framework the other interesting bit happened of course the other contributor
happened of course the other contributor
happened of course the other contributor was Microsoft they created for some of
was Microsoft they created for some of
was Microsoft they created for some of you who are into gaming will know they
you who are into gaming will know they
you who are into gaming will know they created this device called disconnect
created this device called disconnect
created this device called disconnect now what Kinect does is it’s it views it
now what Kinect does is it’s it views it
now what Kinect does is it’s it views it looks at human movements it recognizes
looks at human movements it recognizes
looks at human movements it recognizes human movements and and and mimics those
human movements and and and mimics those
human movements and and and mimics those movements in the computer so basically
movements in the computer so basically
movements in the computer so basically that’s that that’s where a lot of
that’s that that’s where a lot of
that’s that that’s where a lot of progress was made around image
progress was made around image
progress was made around image recognition how is the human moving and
recognition how is the human moving and
recognition how is the human moving and can i replicate it no and I think 2015
can i replicate it no and I think 2015
can i replicate it no and I think 2015 is the sort of year where things started
is the sort of year where things started
is the sort of year where things started moving really fast so you can say it’s
moving really fast so you can say it’s
moving really fast so you can say it’s we’ve reached now cross that point of
we’ve reached now cross that point of
we’ve reached now cross that point of inflection where AI will slowly become
inflection where AI will slowly become
inflection where AI will slowly become mainstream and in 2015 what happened was
mainstream and in 2015 what happened was
mainstream and in 2015 what happened was image recognition algorithms have become
image recognition algorithms have become
image recognition algorithms have become so good there they have now exceeded
so good there they have now exceeded
so good there they have now exceeded their ability to accurately identify
their ability to accurately identify
their ability to accurately identify images compared to humans so now you
images compared to humans so now you
images compared to humans so now you have computers which are able to
have computers which are able to
have computers which are able to recognize images better than humans
recognize images better than humans
recognize images better than humans would that’s that’s why 2015 is the car
would that’s that’s why 2015 is the car
would that’s that’s why 2015 is the car the the year the point of inflection as
the the year the point of inflection as
the the year the point of inflection as far as AI and machine learning is
far as AI and machine learning is
far as AI and machine learning is concerned of course where does it all
concerned of course where does it all
concerned of course where does it all lead to and I’ve mentioned at point
lead to and I’ve mentioned at point
lead to and I’ve mentioned at point of technological singularity so the
of technological singularity so the
of technological singularity so the point so there are people who predict
point so there are people who predict
point so there are people who predict that technology will will or computers
that technology will will or computers
that technology will will or computers will become so good that recursively
will become so good that recursively
will become so good that recursively they’ll become better than what’s
they’ll become better than what’s
they’ll become better than what’s available would become better than the
available would become better than the
available would become better than the human mind by 1945 by 2020 45 okay
human mind by 1945 by 2020 45 okay
human mind by 1945 by 2020 45 okay so I’ll come to the last section of my
so I’ll come to the last section of my
so I’ll come to the last section of my talk which is where where is this all
talk which is where where is this all
talk which is where where is this all leading to so there are multiple models
leading to so there are multiple models
leading to so there are multiple models in place about understanding there is AI
in place about understanding there is AI
in place about understanding there is AI heading so I pointed out what is
heading so I pointed out what is
heading so I pointed out what is classically called as darkness hype
classically called as darkness hype
classically called as darkness hype cycle this is a standard model used to
cycle this is a standard model used to
cycle this is a standard model used to predict where technology’s heading in
predict where technology’s heading in
predict where technology’s heading in the technological world essentially what
the technological world essentially what
the technological world essentially what the model says is that initially there
the model says is that initially there
the model says is that initially there is innovation there is a hype and then
is innovation there is a hype and then
is innovation there is a hype and then there is a dissolution disillusionment
there is a dissolution disillusionment
there is a dissolution disillusionment with the technology because people can’t
with the technology because people can’t
with the technology because people can’t make money out of it that’s the bottom
make money out of it that’s the bottom
make money out of it that’s the bottom line and then over a period of time
line and then over a period of time
line and then over a period of time people still figure out how to learn
people still figure out how to learn
people still figure out how to learn deal with it and then it becomes more
deal with it and then it becomes more
deal with it and then it becomes more mature technology so currently if you go
mature technology so currently if you go
mature technology so currently if you go by the Gartner hype cycle AI and machine
by the Gartner hype cycle AI and machine
by the Gartner hype cycle AI and machine learning are at the hype of their cycle
learning are at the hype of their cycle
learning are at the hype of their cycle which means we are unlikely to see
which means we are unlikely to see
which means we are unlikely to see anything concrete in the immediate
anything concrete in the immediate
anything concrete in the immediate future but in about four to five years
future but in about four to five years
future but in about four to five years time I think AI is going to become all
time I think AI is going to become all
time I think AI is going to become all the more pervasive I have given you
the more pervasive I have given you
the more pervasive I have given you small examples of where AI is used in
small examples of where AI is used in
small examples of where AI is used in that video clip there this is this is my
that video clip there this is this is my
that video clip there this is this is my own phone you can try it if you have if
own phone you can try it if you have if
own phone you can try it if you have if you are an iPhone user you have Siri
you are an iPhone user you have Siri
you are an iPhone user you have Siri which does exactly the same things if
which does exactly the same things if
which does exactly the same things if you are an Android user I just showed
you are an Android user I just showed
you are an Android user I just showed you an example you can talk to your
you an example you can talk to your
you an example you can talk to your phone and so so so I think in another
phone and so so so I think in another
phone and so so so I think in another next five to ten years it this this is
next five to ten years it this this is
next five to ten years it this this is going to become mainstream some of some
going to become mainstream some of some
going to become mainstream some of some other technology be developed here is
other technology be developed here is
other technology be developed here is going to become quite mainstream so
going to become quite mainstream so
going to become quite mainstream so that’s that’s the Gartner hype cycle way
that’s that’s the Gartner hype cycle way
that’s that’s the Gartner hype cycle way of looking at it then there is the other
of looking at it then there is the other
of looking at it then there is the other way of looking at it which is I’m sure
way of looking at it which is I’m sure
way of looking at it which is I’m sure you guys have heard about the the the
you guys have heard about the the the
you guys have heard about the the the big debate between Elon Musk and Mark
big debate between Elon Musk and Mark
big debate between Elon Musk and Mark Zuckerberg Zuckerberg says he is fine
Zuckerberg Zuckerberg says he is fine
Zuckerberg Zuckerberg says he is fine musk says no we must have regulations
musk says no we must have regulations
musk says no we must have regulations around the I the the challenge there is
around the I the the challenge there is
around the I the the challenge there is that with automation history’s big
that with automation history’s big
that with automation history’s big history tells us that with automation we
history tells us that with automation we
history tells us that with automation we lose jobs but jobs are created elsewhere
lose jobs but jobs are created elsewhere
lose jobs but jobs are created elsewhere however with AI what has happened is the
however with AI what has happened is the
however with AI what has happened is the piece of automation has gone drone up
piece of automation has gone drone up
piece of automation has gone drone up explain
explain
explain and that’s that’s where regulation is
and that’s that’s where regulation is
and that’s that’s where regulation is required you might have also heard for
required you might have also heard for
required you might have also heard for example in India the Transport Minister
example in India the Transport Minister
example in India the Transport Minister has said that we are not going to allow
has said that we are not going to allow
has said that we are not going to allow driverless cars because it kills jobs so
driverless cars because it kills jobs so
driverless cars because it kills jobs so I think there will be a feedback to the
I think there will be a feedback to the
I think there will be a feedback to the system yeah I will
system yeah I will
system yeah I will so while the exponents of AI will say we
so while the exponents of AI will say we
so while the exponents of AI will say we will automate everything and we’ll kill
will automate everything and we’ll kill
will automate everything and we’ll kill almost 30 percent of 40 percent of jobs
almost 30 percent of 40 percent of jobs
almost 30 percent of 40 percent of jobs by 2030 or so in the u.s. there were two
by 2030 or so in the u.s. there were two
by 2030 or so in the u.s. there were two computer stock regardless of whether
computer stock regardless of whether
computer stock regardless of whether they’re using AI or not they have they
they’re using AI or not they have they
they’re using AI or not they have they talk to each other using something
talk to each other using something
talk to each other using something called as protocol okay and and in this
called as protocol okay and and in this
called as protocol okay and and in this case the protocol expected protocol was
case the protocol expected protocol was
case the protocol expected protocol was the English language what happened as a
the English language what happened as a
the English language what happened as a consequence of their research was that
consequence of their research was that
consequence of their research was that while the the chat BOTS continued to
while the the chat BOTS continued to
while the the chat BOTS continued to negotiate and they actually became
negotiate and they actually became
negotiate and they actually became better at negotiations they actually
better at negotiations they actually
better at negotiations they actually started using their own language in the
started using their own language in the
started using their own language in the sense that they optimized the English
sense that they optimized the English
sense that they optimized the English words in their own to have their own
words in their own to have their own
words in their own to have their own meaning which was not understood by the
meaning which was not understood by the
meaning which was not understood by the people so they developed their own
people so they developed their own
people so they developed their own protocol so it was nothing fancy and the
protocol so it was nothing fancy and the
protocol so it was nothing fancy and the program was shut down not because it was
program was shut down not because it was
program was shut down not because it was going to create some sort of bots which
going to create some sort of bots which
going to create some sort of bots which would do their own things but the
would do their own things but the
would do their own things but the program was actually shut down because
program was actually shut down because
program was actually shut down because the the bots were not doing what they
the the bots were not doing what they
the the bots were not doing what they were intended to do which is talk to
were intended to do which is talk to
were intended to do which is talk to each other in English language so that
each other in English language so that
each other in English language so that was the story around in the facebook
was the story around in the facebook
was the story around in the facebook chat bots in the end I would like to
chat bots in the end I would like to
chat bots in the end I would like to conclude by saying that AI and machine
conclude by saying that AI and machine
conclude by saying that AI and machine learning is affecting us right now
learning is affecting us right now
learning is affecting us right now having said that in the long term I
having said that in the long term I
having said that in the long term I think it’s the the economically most
think it’s the the economically most
think it’s the the economically most viable things will survive everything
viable things will survive everything
viable things will survive everything else will be thrown on the wayside thank
else will be thrown on the wayside thank
else will be thrown on the wayside thank you
you
you [Applause]
”
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