what do you think of when I say artificial intelligence is it something
artificial intelligence is it something
artificial intelligence is it something like this or maybe this or something
like this or maybe this or something
like this or maybe this or something more like this artificial intelligence
more like this artificial intelligence
more like this artificial intelligence or AI is a subfield of computer science
or AI is a subfield of computer science
or AI is a subfield of computer science which enables intelligence exhibited by
which enables intelligence exhibited by
which enables intelligence exhibited by machines and even experts disagree about
machines and even experts disagree about
machines and even experts disagree about what exactly defines AI but usually they
what exactly defines AI but usually they
what exactly defines AI but usually they can agree that if something exhibits
can agree that if something exhibits
can agree that if something exhibits intelligence if it interacts with its
intelligence if it interacts with its
intelligence if it interacts with its environment in complex ways then we can
environment in complex ways then we can
environment in complex ways then we can generally call it AI how many of you
generally call it AI how many of you
generally call it AI how many of you thought of this when I said artificial
thought of this when I said artificial
thought of this when I said artificial intelligence I work in the White House
intelligence I work in the White House
intelligence I work in the White House in the Office of Science and Technology
in the Office of Science and Technology
in the Office of Science and Technology Policy and the hand on the left belongs
Policy and the hand on the left belongs
Policy and the hand on the left belongs to our President Barack Obama the hand
to our President Barack Obama the hand
to our President Barack Obama the hand on the right belongs to a paralyzed man
on the right belongs to a paralyzed man
on the right belongs to a paralyzed man named Nathan Copeland who’s using a
named Nathan Copeland who’s using a
named Nathan Copeland who’s using a brain computer interface to actually
brain computer interface to actually
brain computer interface to actually control and feel that robotic arm with
control and feel that robotic arm with
control and feel that robotic arm with his mind the Office of Science and
his mind the Office of Science and
his mind the Office of Science and Technology Policy works on some pretty
Technology Policy works on some pretty
Technology Policy works on some pretty amazing things on behalf of the
amazing things on behalf of the
amazing things on behalf of the president the US government and the
president the US government and the
president the US government and the American people I have people in my
American people I have people in my
American people I have people in my office who are working on policy matters
office who are working on policy matters
office who are working on policy matters related to energy and the environment
related to energy and the environment
related to energy and the environment cyber security nanotechnology
cyber security nanotechnology
cyber security nanotechnology biotechnology and much much more
biotechnology and much much more
biotechnology and much much more and my policy portfolio focuses on
and my policy portfolio focuses on
and my policy portfolio focuses on artificial intelligence and robotics so
artificial intelligence and robotics so
artificial intelligence and robotics so I get to wake up every morning and craft
I get to wake up every morning and craft
I get to wake up every morning and craft policy related to AI machine learning
policy related to AI machine learning
policy related to AI machine learning and technology platforms like automated
and technology platforms like automated
and technology platforms like automated vehicles and drones you can think of
vehicles and drones you can think of
vehicles and drones you can think of this talk steward of the schoolhouse
this talk steward of the schoolhouse
this talk steward of the schoolhouse rock with a Jetsons twist
in 2016 the White House Office of
in 2016 the White House Office of Science and Technology Policy started a
Science and Technology Policy started a
Science and Technology Policy started a policy process around artificial
policy process around artificial
policy process around artificial intelligence called the White House
intelligence called the White House
intelligence called the White House future of AI initiative we hosted a
future of AI initiative we hosted a
future of AI initiative we hosted a series of public events five across the
series of public events five across the
series of public events five across the country and we solicited public input on
country and we solicited public input on
country and we solicited public input on all different topics related to AI back
all different topics related to AI back
all different topics related to AI back in October of 2016 we published a
in October of 2016 we published a
in October of 2016 we published a significant public report on the future
significant public report on the future
significant public report on the future of artificial intelligence and a
of artificial intelligence and a
of artificial intelligence and a companion national AI research and
companion national AI research and
companion national AI research and development strategic plan the almost 70
development strategic plan the almost 70
development strategic plan the almost 70 page AI report was a big deal primarily
page AI report was a big deal primarily
page AI report was a big deal primarily because it’s really the most significant
because it’s really the most significant
because it’s really the most significant thing the White House has ever said
thing the White House has ever said
thing the White House has ever said about AI and it’s not the first time
about AI and it’s not the first time
about AI and it’s not the first time that the White House has awoken to the
that the White House has awoken to the
that the White House has awoken to the issue of artificial intelligence
issue of artificial intelligence
issue of artificial intelligence obviously this technology has existed
obviously this technology has existed
obviously this technology has existed for a while now though though the decade
for a while now though though the decade
for a while now though though the decade sorry though though the field of AI has
sorry though though the field of AI has
sorry though though the field of AI has existed for several decades recent
existed for several decades recent
existed for several decades recent progress in AI has been driven by three
progress in AI has been driven by three
progress in AI has been driven by three primary factors that have built upon
primary factors that have built upon
primary factors that have built upon each other the availability of big data
each other the availability of big data
each other the availability of big data which in turn has helped power more
which in turn has helped power more
which in turn has helped power more sophisticated algorithms and machine
sophisticated algorithms and machine
sophisticated algorithms and machine learning techniques and in turn has
learning techniques and in turn has
learning techniques and in turn has relied upon faster computing
relied upon faster computing
relied upon faster computing infrastructure policymaking is a process
infrastructure policymaking is a process
infrastructure policymaking is a process of radical collaboration and the AI
of radical collaboration and the AI
of radical collaboration and the AI policy process for us was no different
policy process for us was no different
policy process for us was no different it is a synthesis of of complex ideas
it is a synthesis of of complex ideas
it is a synthesis of of complex ideas both drawn from within and outside of
both drawn from within and outside of
both drawn from within and outside of the government community and across the
the government community and across the
the government community and across the country we had thousands of people tune
country we had thousands of people tune
country we had thousands of people tune into our AI workshop series we spoke to
into our AI workshop series we spoke to
into our AI workshop series we spoke to people across the public and the private
people across the public and the private
people across the public and the private sectors experts in a variety of
sectors experts in a variety of
sectors experts in a variety of different fields and people from all
different fields and people from all
different fields and people from all different disciplines and backgrounds
the public the White House heard about
the public the White House heard about artificial intelligence and it was via
artificial intelligence and it was via
artificial intelligence and it was via meetings phone calls emails and even
meetings phone calls emails and even
meetings phone calls emails and even faxes yes really faxes from Americans
faxes yes really faxes from Americans
faxes yes really faxes from Americans constituents all across the country and
constituents all across the country and
constituents all across the country and even across the globe the Obama
even across the globe the Obama
even across the globe the Obama administration heard about artificial
administration heard about artificial
administration heard about artificial intelligence and today I’m gonna talk
intelligence and today I’m gonna talk
intelligence and today I’m gonna talk about a few key takeaways from the
about a few key takeaways from the
about a few key takeaways from the lessons that we learned the first is
lessons that we learned the first is
lessons that we learned the first is that we have an imperative to use AI one
that we have an imperative to use AI one
that we have an imperative to use AI one of the most common things we heard from
of the most common things we heard from
of the most common things we heard from people whether they were public citizens
people whether they were public citizens
people whether they were public citizens or experts in the field was that it is
or experts in the field was that it is
or experts in the field was that it is critically necessary that we responsibly
critically necessary that we responsibly
critically necessary that we responsibly develop and deploy as much artificial
develop and deploy as much artificial
develop and deploy as much artificial intelligence as we possibly can
intelligence as we possibly can
intelligence as we possibly can Jason Furman the chair of the
Jason Furman the chair of the
Jason Furman the chair of the President’s Council of Economic Advisers
President’s Council of Economic Advisers
President’s Council of Economic Advisers said that his biggest worry about AI was
said that his biggest worry about AI was
said that his biggest worry about AI was that we don’t have enough of it other
that we don’t have enough of it other
that we don’t have enough of it other experts argued that people are actually
experts argued that people are actually
experts argued that people are actually dying today because we aren’t deploying
dying today because we aren’t deploying
dying today because we aren’t deploying AI in settings where we know it can help
AI in settings where we know it can help
AI in settings where we know it can help save human lives it’s not AI that will
save human lives it’s not AI that will
save human lives it’s not AI that will bring on at the end of humanity they
bring on at the end of humanity they
bring on at the end of humanity they said but the absence of these
said but the absence of these
said but the absence of these technologies that is already killing
technologies that is already killing
technologies that is already killing people and costing lives citing you know
people and costing lives citing you know
people and costing lives citing you know medical errors and ineffective disease
medical errors and ineffective disease
medical errors and ineffective disease treatment as areas where we can apply
treatment as areas where we can apply
treatment as areas where we can apply machine learning techniques to save
machine learning techniques to save
machine learning techniques to save people’s lives in health care ai is
people’s lives in health care ai is
people’s lives in health care ai is helping us predict treat and and help
helping us predict treat and and help
helping us predict treat and and help develop patient care in one example AI
develop patient care in one example AI
develop patient care in one example AI is being used to process vast amounts of
is being used to process vast amounts of
is being used to process vast amounts of oncological data to create a complete
oncological data to create a complete
oncological data to create a complete map of pancreatic cancer so that we can
map of pancreatic cancer so that we can
map of pancreatic cancer so that we can better target these processes with
better target these processes with
better target these processes with treatment mechanisms that can more
treatment mechanisms that can more
treatment mechanisms that can more effectively enabled cancer treatment ai
effectively enabled cancer treatment ai
effectively enabled cancer treatment ai is also helping us save our planet
is also helping us save our planet
is also helping us save our planet researchers are creating an autonomous
researchers are creating an autonomous
researchers are creating an autonomous computational system called wild book
computational system called wild book
computational system called wild book which helping biologists track and
which helping biologists track and
which helping biologists track and evaluate animals for conservation
evaluate animals for conservation
evaluate animals for conservation purposes across the globe using image
purposes across the globe using image
purposes across the globe using image recognition techniques and crowd-sourced
recognition techniques and crowd-sourced
recognition techniques and crowd-sourced publicly available images of all sorts
publicly available images of all sorts
publicly available images of all sorts of Wildlife across the world we can
of Wildlife across the world we can
of Wildlife across the world we can actually identify and track individual
actually identify and track individual
actually identify and track individual animals like zebras elephants and whales
animals like zebras elephants and whales
animals like zebras elephants and whales and this can help us reduce the
and this can help us reduce the
and this can help us reduce the incidence of illegal poaching it can
incidence of illegal poaching it can
incidence of illegal poaching it can help us conserve endangered populations
help us conserve endangered populations
help us conserve endangered populations and can also help us better learn about
and can also help us better learn about
and can also help us better learn about the species with which with which we
the species with which with which we
the species with which with which we share our world and importantly a I can
share our world and importantly a I can
share our world and importantly a I can also help us impact vulnerable
also help us impact vulnerable
also help us impact vulnerable populations by providing critical
populations by providing critical
populations by providing critical assistance in social challenge areas
assistance in social challenge areas
assistance in social challenge areas like poverty and education in one recent
like poverty and education in one recent
like poverty and education in one recent example a school district in Tacoma
example a school district in Tacoma
example a school district in Tacoma Washington partnered with a tech company
Washington partnered with a tech company
Washington partnered with a tech company to develop a machine learning model that
to develop a machine learning model that
to develop a machine learning model that helped analyze student and historical
helped analyze student and historical
helped analyze student and historical data to predict which students were most
data to predict which students were most
data to predict which students were most at risk of dropping out of school and
at risk of dropping out of school and
at risk of dropping out of school and prompt early intervention and after a
prompt early intervention and after a
prompt early intervention and after a multi-year pilot of that system they
multi-year pilot of that system they
multi-year pilot of that system they actually were able to boost graduation
actually were able to boost graduation
actually were able to boost graduation rates from 55% to 78 percent in just
rates from 55% to 78 percent in just
rates from 55% to 78 percent in just four years
AI is impacting all areas of society and
AI is impacting all areas of society and it’s clear from these examples and many
it’s clear from these examples and many
it’s clear from these examples and many others that that’s the case we also
others that that’s the case we also
others that that’s the case we also learned that it’s critical for everyone
learned that it’s critical for everyone
learned that it’s critical for everyone to develop and benefit from AI it’s a
to develop and benefit from AI it’s a
to develop and benefit from AI it’s a pretty normal day if I am one of the
pretty normal day if I am one of the
pretty normal day if I am one of the only woman in a lot of my meetings and
only woman in a lot of my meetings and
only woman in a lot of my meetings and that story isn’t new not in the tech
that story isn’t new not in the tech
that story isn’t new not in the tech sector and certainly not in the field of
sector and certainly not in the field of
sector and certainly not in the field of computer science unfortunately the field
computer science unfortunately the field
computer science unfortunately the field of AI is even more homogeneous today
of AI is even more homogeneous today
of AI is even more homogeneous today just 18 percent of graduates and
just 18 percent of graduates and
just 18 percent of graduates and computer science are female down from a
computer science are female down from a
computer science are female down from a peak of 37 percent in 1984 and the field
peak of 37 percent in 1984 and the field
peak of 37 percent in 1984 and the field of AI is even less diverse at a recent
of AI is even less diverse at a recent
of AI is even less diverse at a recent neural information processing systems
neural information processing systems
neural information processing systems conference in 2015 which is one of the
conference in 2015 which is one of the
conference in 2015 which is one of the largest conferences on AI research in
largest conferences on AI research in
largest conferences on AI research in the world just 13 percent of
the world just 13 percent of
the world just 13 percent of participants were female blacks Latinos
participants were female blacks Latinos
participants were female blacks Latinos and members of other ethnic and racial
and members of other ethnic and racial
and members of other ethnic and racial minority groups are also severely
minority groups are also severely
minority groups are also severely underrepresented compared to their
underrepresented compared to their
underrepresented compared to their shares of the u.s. population in stem
shares of the u.s. population in stem
shares of the u.s. population in stem and the tech workforce and certainly in
and the tech workforce and certainly in
and the tech workforce and certainly in the field of computer science and the
the field of computer science and the
the field of computer science and the subfield of AI as machine learning
subfield of AI as machine learning
subfield of AI as machine learning techniques become more ubiquitous and we
techniques become more ubiquitous and we
techniques become more ubiquitous and we use them more and more in everyday life
use them more and more in everyday life
use them more and more in everyday life to replace human decision-making
to replace human decision-making
to replace human decision-making it becomes increasingly important for us
it becomes increasingly important for us
it becomes increasingly important for us to get this technology right last year
to get this technology right last year
to get this technology right last year users of a popular photo app which
users of a popular photo app which
users of a popular photo app which automatically applied tags to digital
automatically applied tags to digital
automatically applied tags to digital photos and albums discovered that it was
photos and albums discovered that it was
photos and albums discovered that it was actually classifying images of black
actually classifying images of black
actually classifying images of black people as gorillas machine learning
people as gorillas machine learning
people as gorillas machine learning often reflects biases that exist in
often reflects biases that exist in
often reflects biases that exist in everyday life in this case it was
everyday life in this case it was
everyday life in this case it was fundamentally a data problem but it also
fundamentally a data problem but it also
fundamentally a data problem but it also impacts algorithms and the outputs of AI
impacts algorithms and the outputs of AI
impacts algorithms and the outputs of AI systems it is critically important and
systems it is critically important and
systems it is critically important and we all have a responsibility for us to
we all have a responsibility for us to
we all have a responsibility for us to ensure that AI systems reflect diverse
ensure that AI systems reflect diverse
ensure that AI systems reflect diverse populations and that we help reduce the
populations and that we help reduce the
populations and that we help reduce the negative impacts of narrowly focused AI
negative impacts of narrowly focused AI
negative impacts of narrowly focused AI development and unintended biases in AI
development and unintended biases in AI
development and unintended biases in AI systems the third lesson that we learned
systems the third lesson that we learned
systems the third lesson that we learned is that understanding AI can be hard but
is that understanding AI can be hard but
is that understanding AI can be hard but is deeply important I don’t know if any
is deeply important I don’t know if any
is deeply important I don’t know if any of you have seen this video of a robot
of you have seen this video of a robot
of you have seen this video of a robot named Atlas by Massachusetts firm Boston
named Atlas by Massachusetts firm Boston
named Atlas by Massachusetts firm Boston Dynamics but for those of you who
Dynamics but for those of you who
Dynamics but for those of you who haven’t it depicts a robot stumbling
haven’t it depicts a robot stumbling
haven’t it depicts a robot stumbling through a snowy field with dexterity
through a snowy field with dexterity
through a snowy field with dexterity than somebody like me probably could and
than somebody like me probably could and
than somebody like me probably could and getting pushed over by a person only to
getting pushed over by a person only to
getting pushed over by a person only to spring back up again with an almost
spring back up again with an almost
spring back up again with an almost disturbing resilience when this video
disturbing resilience when this video
disturbing resilience when this video came online last winter I got a call
came online last winter I got a call
came online last winter I got a call from my grandfather who had never before
from my grandfather who had never before
from my grandfather who had never before talked to me about policy matters
talked to me about policy matters
talked to me about policy matters related to my job what are you doing
related to my job what are you doing
related to my job what are you doing about this robot he said so it had
about this robot he said so it had
about this robot he said so it had personally affronted him somehow from
personally affronted him somehow from
personally affronted him somehow from the computer screen on which he was
the computer screen on which he was
the computer screen on which he was watching it I think this video was an
watching it I think this video was an
watching it I think this video was an important lesson for a lot of people
important lesson for a lot of people
important lesson for a lot of people because it made especially salient the
because it made especially salient the
because it made especially salient the current state of Technology but it was
current state of Technology but it was
current state of Technology but it was also a critical lesson for me in my
also a critical lesson for me in my
also a critical lesson for me in my position at the time because it also
position at the time because it also
position at the time because it also helped me realize the potential
helped me realize the potential
helped me realize the potential disconnect that exists between the
disconnect that exists between the
disconnect that exists between the people working on the science and the
people working on the science and the
people working on the science and the policies behind these technologies and
policies behind these technologies and
policies behind these technologies and everyone else and it turns out that
everyone else and it turns out that
everyone else and it turns out that people vary in how well they understand
people vary in how well they understand
people vary in how well they understand AI in a recent global consumer survey by
AI in a recent global consumer survey by
AI in a recent global consumer survey by Weber shanwick and KRC research over
Weber shanwick and KRC research over
Weber shanwick and KRC research over under 20% of
under 20% of
under 20% of global survey participants actually so
global survey participants actually so
global survey participants actually so that they understand a lot about AI and
that they understand a lot about AI and
that they understand a lot about AI and over 30 percent actually admitted to not
over 30 percent actually admitted to not
over 30 percent actually admitted to not knowing anything so you’re not alone if
knowing anything so you’re not alone if
knowing anything so you’re not alone if you don’t really understand this field
you don’t really understand this field
you don’t really understand this field of Technology interestingly though
of Technology interestingly though
of Technology interestingly though despite of this we are pretty positive
despite of this we are pretty positive
despite of this we are pretty positive about the overall impact of artificial
about the overall impact of artificial
about the overall impact of artificial intelligence forty five percent of us
intelligence forty five percent of us
intelligence forty five percent of us think it’s gonna be positive and only
think it’s gonna be positive and only
think it’s gonna be positive and only seven percent of us think it’s gonna be
seven percent of us think it’s gonna be
seven percent of us think it’s gonna be negative and fifty two percent of
negative and fifty two percent of
negative and fifty two percent of consumers believe that AI will have a
consumers believe that AI will have a
consumers believe that AI will have a positive impact on their lives
positive impact on their lives
positive impact on their lives personally the fourth lesson that we
personally the fourth lesson that we
personally the fourth lesson that we learned is that agile government is
learned is that agile government is
learned is that agile government is critically important to the responsible
critically important to the responsible
critically important to the responsible development of artificial intelligence
over the course of the development of
over the course of the development of the AI report we learned a lot of things
the AI report we learned a lot of things
the AI report we learned a lot of things but this was perhaps the most salient
but this was perhaps the most salient
but this was perhaps the most salient there are many areas where it doesn’t
there are many areas where it doesn’t
there are many areas where it doesn’t seem like government has direct
seem like government has direct
seem like government has direct influence over AI as it’s related to
influence over AI as it’s related to
influence over AI as it’s related to governance issues but this is actually
governance issues but this is actually
governance issues but this is actually not the case today the government’s
not the case today the government’s
not the case today the government’s making policy decisions about AI that
making policy decisions about AI that
making policy decisions about AI that actually impact the way that this
actually impact the way that this
actually impact the way that this technology is developed and will be used
technology is developed and will be used
technology is developed and will be used in every everyday circumstances like
in every everyday circumstances like
in every everyday circumstances like people like me and you and there’s no
people like me and you and there’s no
people like me and you and there’s no area where this is more relevant than in
area where this is more relevant than in
area where this is more relevant than in intelligent transportation which is an
intelligent transportation which is an
intelligent transportation which is an area that my policy portfolio focuses
area that my policy portfolio focuses
area that my policy portfolio focuses intently upon last month we had US
intently upon last month we had US
intently upon last month we had US Transportation Secretary Anthony Foxx
Transportation Secretary Anthony Foxx
Transportation Secretary Anthony Foxx come in and talk to us about intelligent
come in and talk to us about intelligent
come in and talk to us about intelligent transportation platforms and when asked
transportation platforms and when asked
transportation platforms and when asked how many questions he had received over
how many questions he had received over
how many questions he had received over the course of his confirmation hearing
the course of his confirmation hearing
the course of his confirmation hearing on either automated vehicles or drones
on either automated vehicles or drones
on either automated vehicles or drones the answer he gave was zero and that was
the answer he gave was zero and that was
the answer he gave was zero and that was three years ago
three years ago
three years ago in 2013 this highlights the critical
in 2013 this highlights the critical
in 2013 this highlights the critical importance that policymakers keep in
importance that policymakers keep in
importance that policymakers keep in mind the current state of technology and
mind the current state of technology and
mind the current state of technology and keep up with the pace of it in
keep up with the pace of it in
keep up with the pace of it in developing policy and regulatory issues
developing policy and regulatory issues
developing policy and regulatory issues related to it
related to it
related to it neither issue was on the map he said my
neither issue was on the map he said my
neither issue was on the map he said my concern has been that we’re moving into
concern has been that we’re moving into
concern has been that we’re moving into the Jetsons era but we have a flint we
the Jetsons era but we have a flint we
the Jetsons era but we have a flint we have Flintstone approaches to authority
have Flintstone approaches to authority
have Flintstone approaches to authority and regulation and we can’t go the
and regulation and we can’t go the
and regulation and we can’t go the distance with this until we really think
distance with this until we really think
distance with this until we really think about things differently transportation
about things differently transportation
about things differently transportation policy is something we took a deep look
policy is something we took a deep look
policy is something we took a deep look at when we were writing the
at when we were writing the
at when we were writing the administration’s AI report and there are
administration’s AI report and there are
administration’s AI report and there are several reasons for it the first is that
several reasons for it the first is that
several reasons for it the first is that transportation affects everybody
transportation affects everybody
transportation affects everybody if you ever drive in a car or have
if you ever drive in a car or have
if you ever drive in a car or have written an aircraft
written an aircraft
written an aircraft get from point A to point B if you are
get from point A to point B if you are
get from point A to point B if you are an owner or operator of one of the seven
an owner or operator of one of the seven
an owner or operator of one of the seven million drones projected to be in US
million drones projected to be in US
million drones projected to be in US airspace by 2020 this policy area
airspace by 2020 this policy area
airspace by 2020 this policy area affects you when we were developing the
affects you when we were developing the
affects you when we were developing the AI report one of the biggest lessons as
AI report one of the biggest lessons as
AI report one of the biggest lessons as I mentioned was that we keep in mind the
I mentioned was that we keep in mind the
I mentioned was that we keep in mind the pace of Technology automated vehicles
pace of Technology automated vehicles
pace of Technology automated vehicles have the potential to save almost all
have the potential to save almost all
have the potential to save almost all the 35,000 deaths caused a year by
the 35,000 deaths caused a year by
the 35,000 deaths caused a year by traffic accidents and to totally
traffic accidents and to totally
traffic accidents and to totally transform the way that we organize our
transform the way that we organize our
transform the way that we organize our communities and allocate our time
communities and allocate our time
communities and allocate our time autonomous aircraft systems are already
autonomous aircraft systems are already
autonomous aircraft systems are already changing the ways in which we observe
changing the ways in which we observe
changing the ways in which we observe and conserve our environment helping us
and conserve our environment helping us
and conserve our environment helping us save lives and inspect critical
save lives and inspect critical
save lives and inspect critical infrastructure it’s increasingly
infrastructure it’s increasingly
infrastructure it’s increasingly important as technology helps enable
important as technology helps enable
important as technology helps enable these critical social changes that we
these critical social changes that we
these critical social changes that we help enable it equally from the
help enable it equally from the
help enable it equally from the perspective of the government I want to
perspective of the government I want to
perspective of the government I want to end with a focus on one critical
end with a focus on one critical
end with a focus on one critical question which informs everything that
question which informs everything that
question which informs everything that we do it’s scrawled on a whiteboard in
we do it’s scrawled on a whiteboard in
we do it’s scrawled on a whiteboard in our office actually and that question is
our office actually and that question is
our office actually and that question is how does they’re going to keep the
how does they’re going to keep the
how does they’re going to keep the future in the present tense at the
future in the present tense at the
future in the present tense at the beginning of this talk I spoke of the
beginning of this talk I spoke of the
beginning of this talk I spoke of the notion of radical collaboration which
notion of radical collaboration which
notion of radical collaboration which existed between policymakers and the
existed between policymakers and the
existed between policymakers and the public community that we are affecting
public community that we are affecting
public community that we are affecting and I want to underscore how critical it
and I want to underscore how critical it
and I want to underscore how critical it is that policymaking remains a
is that policymaking remains a
is that policymaking remains a team-building process that means that
team-building process that means that
team-building process that means that everyone has to have a seat at the table
everyone has to have a seat at the table
everyone has to have a seat at the table it’s more than just bureaucratic
it’s more than just bureaucratic
it’s more than just bureaucratic optimism that I’m saying this we
optimism that I’m saying this we
optimism that I’m saying this we literally cannot do it without you
literally cannot do it without you
literally cannot do it without you we need academia industry and experts in
we need academia industry and experts in
we need academia industry and experts in a variety of different communities to
a variety of different communities to
a variety of different communities to step up and provide leadership and
step up and provide leadership and
step up and provide leadership and educating the American public and the
educating the American public and the
educating the American public and the next generation of innovators about the
next generation of innovators about the
next generation of innovators about the importance of AI for good
importance of AI for good
importance of AI for good we need the advocacy community the media
we need the advocacy community the media
we need the advocacy community the media and people from a wider range of other
and people from a wider range of other
and people from a wider range of other backgrounds to hold developers
backgrounds to hold developers
backgrounds to hold developers accountable to AI systems so that they
accountable to AI systems so that they
accountable to AI systems so that they responsibly represent the needs of a
responsibly represent the needs of a
responsibly represent the needs of a diverse variety of populations we need
diverse variety of populations we need
diverse variety of populations we need more woman and underrepresented to STEM
more woman and underrepresented to STEM
more woman and underrepresented to STEM fields and the AI disciplines and we
fields and the AI disciplines and we
fields and the AI disciplines and we need more people to reflect more often
need more people to reflect more often
need more people to reflect more often about how these technologies impact
about how these technologies impact
about how these technologies impact policymaking on a regular basis we need
policymaking on a regular basis we need
policymaking on a regular basis we need all of you artificial intelligence
all of you artificial intelligence
all of you artificial intelligence has incredible potential if only we all
has incredible potential if only we all
has incredible potential if only we all work together to unlock it now more than
work together to unlock it now more than
work together to unlock it now more than ever before
ever before
ever before we need people from all backgrounds and
we need people from all backgrounds and
we need people from all backgrounds and all disciplines to show up engage with
all disciplines to show up engage with
all disciplines to show up engage with each other in with their government and
each other in with their government and
each other in with their government and help us craft a more inclusive
help us craft a more inclusive
help us craft a more inclusive forward-leaning future which helps move
forward-leaning future which helps move
forward-leaning future which helps move us all in the right direction
us all in the right direction
us all in the right direction thank you
thank you
thank you [Applause]
”
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