April 14, 2025 • 20 mins
While artificial intelligence-driven technology is promising practical solutions to global challenges, AI-driven research advances the frontiers of knowledge and propels American ingenuity. Sethuraman Panchanathan, the 15th director of the U.S. National Science Foundation, discusses the current state of AI and the many ways it may be used in the future.
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(00:03):
This is the Discovery Files podcastfrom the U.S.
National Science Foundation.
Artificial
intelligence is becoming ubiquitous,transforming Americans daily lives.
AI driven technology is promisingpractical solutions
to global challenges, from agricultureto health care and education.
NSF supported AI research advancesbreakthroughs that push the frontiers
(00:25):
of knowledge, benefit people,and are aligned to the needs of society.
We're joined by Dr. SethuramanPanchanathan,
the 15th director of the U.S.
National Science Foundation.
Dr. Panchanathan has more than three
decades of experienceas a computer scientist and engineer,
where he's contributed to advancingresearch, innovation, strategic
partnerships, entrepreneurship, globaldevelopment, and economic growth.
(00:46):
Dr. Panchanathan,thank you so much for joining me today.
Thank you. It's great to be with you.
I'd like to startwith a little bit of background.
And in your case we're talking aboutAI today.
And I want to get into your computervision background a little bit.
Could you tell us what computer vision isand how do we see it in the devices
we use every day?
You know, the best way of describingcomputer vision is, I mean, we know vision
because we all have the ability.
(01:07):
Most of us have the gift of sight. Right?
And so if you look at the computer,therefore,
having the ability to perceive the world
around us through the capabilitiesthat we have through our vision,
and that would then qualify as computervision in the sense that you're processing
visual information that is aroundyou and be able to make some meaningful
(01:27):
decisionsbased on the understanding that you have
processing the visual information.
So that would be thought of as computervision writ large.
So it's very important to kind ofthe architecture of how AI is working
right now.
It is because AI is essentiallya mechanism by which what you understand
then helps you to be able to
(01:48):
gain knowledgeand use that knowledge for further
making the correlations connections,and be able to help advance
any application that you are tryingto advance if, let's say, transportation.
So you're able to gather the informationto computer vision.
You have an understanding ofwhat is happening.
Imagine you're driving your carwith the many cameras that you have.
(02:10):
It understands what is in front of you
and what is on your side,and even the back through the cameras.
And you acquire the information.
You process itand then you gather from that.
So the features that you process,
you get an understandingof what is happening around you.
And what then AI does is able to then
learn from what is being processed
(02:30):
so that it can be used for navigating you
through terrains that may be already,terrains that you've seen, or trains
that are unknown, and being able to drawthe correspondences from the terrain
that we have known before to terrains thatare similar or things that are unknown.
And you're trying to make connectionsto the unknown tenants.
So that's the kind of thingthat I provides you,
(02:52):
the abilityto be able to learn from the data,
to be able to predict,to be able to help make decisions.
So these are the things where I as a field
is, you know,augmenting the vision capabilities.
And so the computer visionand AI then becomes a much more powerful
way of being ableto perceive the environment and act on it.
(03:14):
So we kind of contextualize thatwith vehicles
and how it's kind of analyzingthe environment for people
that are just hearing the termartificial intelligence.
Can you kind of explainwhat it actually is?
No, it is basically that,you know, when we as humans,
when we see something, you classifywhatever you see.
When I see you, for example,if I've seen you before, I recognize you.
(03:38):
If I have not seen you before,then I say, oh, he looks like this person.
Or you attach a label to the new personthat you're seeing.
Even though you may classify it as closeas something you say, now I know him,
and I know that this is what his name isand this is how he looks.
And so all of the features that you havewith your name then get attached, right.
(04:01):
And so now if I start doing thisfor all the things that they encounter,
and that helps me in recognition,but also it helps me in things
that I have not seen before and be ableto classify to the nearest possible match.
And this kind of a way inwhich you are learning from the data.
So here we are talking about visionand recognition through vision.
(04:23):
But it doesn't have to be a vision,any data for that matter.
The more you're able to trainbased on the data that you have seen
and that's what you do as a human,then you use that for
not only understanding the dataor recognizing similar things patterns,
but you're also able to navigate to thingsthat you might not have seen before,
but you have a sense of whatit might be like based on that learning.
(04:47):
So in a sense, you can say AI is basically
a set of learning prediction
and therefore being able to make progressin terms of making decisions.
But it's mostly about how you're ableto take
the informationthat is at hand, process them,
and then build models based on thatso that learning
(05:10):
those models can help you navigatethrough new data on a similar data,
or even exactly that data in,depending on what the outcome is.
Whether you want to recognizeor find the closest thing that is to it,
or be able to build thingswith what you have learned
and be able to now build thingsbased on what you have learned.
So you said progress there,and I want to think about the future.
(05:31):
Can you tell us some of the thingsthat I could be used for?
Everything that you can imaginein the world around you, because it's like
asking the question, you know,but what can a human be useful for?
The question is no different, isn't it?
Once you have a person who is capable of
doing something, then you say thatthis person can do these things.
So you take any task,whether it is a driving task,
(05:54):
whether it is a task that is based on
predicting the weather or whether it isa task based on how do I learn,
what do I need to learn with thatwhich I don't know anything about?
You take any task for that matter.
You can always find a waythat I can be useful in that context,
because at the end of the day,it is all about data.
Learning from the data, building modelsand then using those models,
(06:16):
then being able to predictbased on what is not known yet.
So these are the kinds of thingsthat you do in every aspect of your life.
I mean, you look at your everyday activity
right from the time that you wake upuntil you go to sleep.
Every activity that you encounteris based on some kind of a recognition
pattern, recognitionas some kind of a decision that you make
or some kind of a learning that you havethat helps you for the future,
(06:38):
and some kind of setof some new hypotheses that you double
based on the informationthat you have gathered.
All of these things are what you doevery day in your life,
from morning to night.
One or more of these kinds of things.
So I, for me, is exactly similar to that.
It's now an agent instead of a humanwho is essentially facing
the same set of things. A lot of data.
(07:00):
There are existing models.
You build new models or refinethe models, use it for recognition
or using it for developingthe closest match or using it
for developing newer hypothesis
or helping us in making decisions.
So all of these things are what we doas humans,
(07:21):
and therefore I can do similar thingsin any field that you can think about.
Right? Is part of the challenge
then deciding what fieldsit's most beneficial to use it.
I mean.
It depends on which field where you needthe most help in, and particularly
in terms of being able to automatemore easily, if you could put it that way.
So the low hanging fruit opportunitiesmight be easier to build models
(07:43):
with a small amount of datastill be able to do meaningful things,
but that does not preclude usfrom building these mega models
that you hear about.
ChatGPT and others, which can be usefulfor a variety of other applications.
So at the end of the day,it is not limited,
but it depends on whatyou want to apply that for
and with what level of efficiencyand efficacy that you want that model to
(08:04):
work for you, or that particular solutionyou want to work for you.
And that's what it is based on.
And so if you take health,there are aspects of health
that can benefit from using AI inin all the way from understanding
the genome to all the way to deliveringpatient care, individualized personalized
patient care.
If you take health,if you take transportation,
you can have a lot of automationbuilt into the transportation.
(08:24):
You can have a lot of safety featuresbuilt because of the automation
that is there in transportation.
It might help in terms ofspeeding up things versus where they are
right now in terms of what you can do withexisting transportation infrastructures.
So it could be in transportation.
It could be in learning.
A student now can find thatthey can do anything in terms
of advancing their learning aspirationsby being able to find what are those
(08:47):
specific things that you findthat you don't understand.
Well, now, for example,if you're taking Calculus Course,
let's say that some of the calculus courserequires you
to have an understandingof mathematical concepts
that were in your priormathematics courses,
but some of which you are very familiarwith.
Some of thisyou find that you either did not learn
well or you've forgottenwhatever that might be.
(09:10):
And so what happens is,depending on what you might need,
you could go back and get the appropriatethings filled up
so they are able to understandnew concept them.
So essentiallyit becomes like your partner if you, me,
you and the machine in this caseAI are working together in terms
of being able to expand your capabilitiesor your effectiveness.
(09:31):
And so that'swhat makes it so interesting, exciting
in whatever field it isin, you can find something
that will enrich you, empoweryou, augment you,
make you a lot more effectiveand efficient.
There's a lot of fear with peopleand their job security
and how this might impact that.
(09:51):
Can you talk about some of the waysI seem to be able to be worked alongside?
How will it be a toolthat benefits workers everywhere?
That's a good question.
Again, it depends on the work.
Is such a broad thing right?
There are so many dimensionsand classifications
of the types of workin every type of work.
(10:12):
It doesn't matter whether you'rea physician or a physician assistant,
if you are a student,a tutor, or a professor,
if you are a truck driveror you're an automobile designer,
it doesn't matterwhat your type of workers.
You can always find those thingswhere the AI tools can be very helpful
to you to enhance your capabilitiesand abilities.
(10:36):
And I would say that the things
that you may not be doing right now,which you are capable of
because you're doing the thingsthat otherwise occupy you.
If you're unburdened by those things,
then you are able to now useyour skill sets even more,
and maybe your creative mindsetsare expanded even more,
(10:58):
and artistscan now do even more creative art.
A physician can do amazing diagnosis andtreatment beyond what they are right now.
All of these are possible because younow this becomes a companion for you.
So in a sense, when people worryabout all, is my job going to be lost?
It is not that you lose.
Your job necessarily is always the case.
(11:18):
Yes, in some casesthat might be the case, right?
A machine may be an AI devicemay be able to do that what you do.
But to methat frees you up to doing other things.
Now the pace of progressclearly is much faster than it was before.
I mean, you could arguethat similar thing was true in in
when tellers were thereand then the ATM machines came about is
(11:40):
so you could think of itthat those who are tellers,
the 50 of them in a bank now, maybethere's 1 or 2 with the ATM machine.
Let us say on what you doelectronically with your mobile device.
But those 48other people have found other ways
in which they are using the talentsand skills, not necessarily
only in the bank environment,but in other environments.
(12:00):
This is what will happen at AI too,is that the jobs of today,
some of them will be augmented,enriched, enhanced.
Some of them will be lost, in which case
people will naturally gravitateto learning those skill sets and mindsets
that they will expand to being ableto create the new jobs of the future,
or to do the new jobs of the future.
(12:20):
So it's very hard to say,by looking at only a static picture
of where we are and saying,what I am doing today, be
gone is a very narrow perspective throughwhich you look at the world, right?
It's much, much,much more broader than that.
Now comes with the associated questionsand things that we ought to do.
So there is no longer this feelingthat, you know, you had this
(12:42):
18 years of educationor 21 years of education
or 25 years of education,whatever that is that you do.
And then you go take a joband then you stay with the job.
And that was no longer true. Even,you know, a couple of decades ago.
And it's no more becomingless and less true now. Right?
So you
can imagine that for that,learning is a lifelong pursuit.
You're constantly upskilling, reskilling,retooling, learning new things.
(13:07):
So it is not like, you know, let me finishmy studies and then I have my work.
It's the learning and work and learninghave become so intertwined
that it is a symbiotic.
It's kind of an activity.
And in that kind of a scenario,nobody's outdated.
No skill becomes unnecessarybecause you have to acquire new skills,
(13:27):
so your skill becomes different.
It constantly shapes, modulates itselfand so on.
So that's somethingthat is very interesting
as a paradigm shift,if you want to look at it that way.
But what is even more excitingis that you can think of the kind of jobs
that you were doing that you felt like,why am I doing all of this
is now going to be replaced by machinesthat can do the job
(13:48):
that allows you to express your skill,ability, creativity,
and other kinds of abilitiesthat you have to be able
to express in its fullest form.
And we may not even know what they are.
It's some of these thingsmay not have even come out of you
because you've been anonymousabout boredom, but
you have been engaged in those activities
(14:09):
that have not let your creativitymanifest itself in its fullest form.
Human talent is capable of doing a lot,lot more than what we think.
So I think I want to look at itfrom that futuristic perspective,
because if you look at anything
which is empowered by technologywill be thought of as,
oh my God, this is going to take awaysomething from me.
(14:29):
But that is only by imagining yourselfas that
just static picture of today,but not envisioning what it could be.
The tomorrow.
So thinking about tomorrow,I want to ask you about how NSF
is working to kind of guidehow AI is developed in the United States.
Let the questions so clearly you said,what area is this?
(14:49):
I have an impact.
And I almost answered the question,missing everything and anything.
And you know what kind of impactit will have on humans.
We talked about the potential potentials,you know, amazing.
And and then it could be enormous.
We talked aboutwhat kind of things it will do for jobs,
answered by saying that it can empoweryou, enrich you, and so on.
(15:10):
So when you look at that,NSF is engaging every dimension
of all of this,all the way from looking at
if you need to have a device,the device starts with understanding what
the materials are to building the devices,to building the technology.
And you look at the physics of it,the chemistry of it,
(15:32):
the mathematics of it, and the engineeringof it, all of that and the design of it,
all of that is something that NSFmakes possible through its various tweaks.
Then you look at all the applicationsthat it can be put to use, whether it is,
a geoscience application like,you know, it could be exploring the planet
and finding solutions for challengeslike climate and mitigation,
(15:53):
climate adaptation and so on.
It could be that
or it could be a bioscience problemthat you're trying to understand
the fundamental basis of how the humanbody functions all the way from a cell.
Right.
And how might you personalize strategiesfor having a good health
outcome all the time?
A bioscience structure,it has those kinds of things.
How can you build better environments?
(16:13):
By having good understandingof biology and, and so on.
Then you look at our social,behavioral, economic scientist directorate
and all the work
that NSF does is at the end of the day,it is all about interfacing with the human
and all the aspectsthat come with the social, the behavioral,
the ethics, the policy,all of that is all centered
around the work that the socialeconomic sciences director works on.
(16:37):
And as I said, the mathematicalphysical center structure.
And if you look at applications
like astronomy, understandingwhere we came from, right.
Again, all of that and more iswhat NSF is constantly engaged in.
And when we talk about educatingthe future,
the talent of the future,
again, our education Directorateand all of the directorates are engaged
with educating, you know, helpingwith educating better K-12 students,
(16:59):
better undergraduate students, graduatestudents, better community college
students, skill sets that there areall these students build better research
and then all constantlyin all of these things,
pushing the boundaries of discovery,
okay.
And then providing the platformsthat allow all of this
to manifest itself in terms of betterindustries of the future,
(17:20):
that are entrepreneurial outcomes,better entrepreneurs, all of these things.
I mean, you couldI can go into every aspect of it.
You will find.
That's why NSF is a unique agency.
It touches pretty much every aspect
of what is needed for us to deliverthe futures that we are talking about.
As much as I as a field is important,
but I get touched by and influences
(17:43):
a host of other disciplines areas,and so therefore
it is a comprehensive picture,not just only what we do in the computing
informationsciences, engineering directorate,
but as you know, AI has been involvedfor the last several decades over.
Something like 50 years.
So 50, 50, 60 years,but also in the investing in AI Institute.
Right.
Which means that we are lookingat every possible avenue
(18:06):
by which I can be furtherpropelled into the future.
But what I can do to propel thingsinto the future,
these AI institutes are tremendousinvestments, right?
That's another thingthat that you have seen that we do here.
So if you look at the collectiveinvestments in AI around AI
and what NSF makes writ largein, in various,
(18:26):
fields of science, engineeringand technology, you find that all of
that means something for the advancementof this into the future.
For the final question today,I want to ask you about the future.
What about how AI is developing excitesyou the most?
I think what excites me the most is,you know, I've always believed
the human potential is enormous.
(18:46):
But yet if you look at globallyin our nation to but globally,
I don't think we have been ableto exercise all of the human ingenuity,
the innovative mindsetand the human spirit.
And so what I and present to usthe opportunity to be able
to unleash all of the human potentialfor the benefit of humanity and beyond,
(19:07):
and that, to me is very excitingthat we will be able to do that
at scale and at speed and everywhere,not limited to a few locations.
That really makes it very exciting.
Any minute. Imagine,let's take our nation.
Imagine every area,rural area, every urban area,
all the 50 states and territoriesof our country and people everywhere
(19:31):
being empowered, energized to be ableto exercise their talent to the fullest
without any holding back,without any constraints.
Imagine that that can do amazing thingsfor our nation, build
tremendous prosperity for our nation
and through that, and have prosperitypossible all across the globe.
Specialthanks to Doctor Sethuraman Panchanathan.
For The Discovery files, I'm Nate Pottker.
(19:52):
You can watch video versions
of these conversations on our YouTubechannel by searching @NSFscience.
Please subscribe wherever you get podcastsand if you like our program,
share with a friendand consider leaving a review.
Discover how the U.S.
National Science Foundationis advancing research at NSF.gov.
This is the Discovery Files podcastfrom the U.S.
National Science Foundation.
Artificial
intelligence is becoming ubiquitous,transforming Americans daily lives.
AI driven technology is promisingpractical solutions
to global challenges, from agricultureto health care and education.
NSF supported AI research advancesbreakthroughs that push the frontiers
(00:25):
of knowledge, benefit people,and are aligned to the needs of society.
We're joined by Dr. SethuramanPanchanathan,
the 15th director of the U.S.
National Science Foundation.
Dr. Panchanathan has more than three
decades of experienceas a computer scientist and engineer,
where he's contributed to advancingresearch, innovation, strategic
partnerships, entrepreneurship, globaldevelopment, and economic growth.
(00:46):
Dr. Panchanathan,thank you so much for joining me today.
Thank you. It's great to be with you.
I'd like to startwith a little bit of background.
And in your case we're talking aboutAI today.
And I want to get into your computervision background a little bit.
Could you tell us what computer vision isand how do we see it in the devices
we use every day?
You know, the best way of describingcomputer vision is, I mean, we know vision
because we all have the ability.
(01:07):
Most of us have the gift of sight. Right?
And so if you look at the computer,therefore,
having the ability to perceive the world
around us through the capabilitiesthat we have through our vision,
and that would then qualify as computervision in the sense that you're processing
visual information that is aroundyou and be able to make some meaningful
(01:27):
decisionsbased on the understanding that you have
processing the visual information.
So that would be thought of as computervision writ large.
So it's very important to kind ofthe architecture of how AI is working
right now.
It is because AI is essentiallya mechanism by which what you understand
then helps you to be able to
(01:48):
gain knowledgeand use that knowledge for further
making the correlations connections,and be able to help advance
any application that you are tryingto advance if, let's say, transportation.
So you're able to gather the informationto computer vision.
You have an understanding ofwhat is happening.
Imagine you're driving your carwith the many cameras that you have.
(02:10):
It understands what is in front of you
and what is on your side,and even the back through the cameras.
And you acquire the information.
You process itand then you gather from that.
So the features that you process,
you get an understandingof what is happening around you.
And what then AI does is able to then
learn from what is being processed
(02:30):
so that it can be used for navigating you
through terrains that may be already,terrains that you've seen, or trains
that are unknown, and being able to drawthe correspondences from the terrain
that we have known before to terrains thatare similar or things that are unknown.
And you're trying to make connectionsto the unknown tenants.
So that's the kind of thingthat I provides you,
(02:52):
the abilityto be able to learn from the data,
to be able to predict,to be able to help make decisions.
So these are the things where I as a field
is, you know,augmenting the vision capabilities.
And so the computer visionand AI then becomes a much more powerful
way of being ableto perceive the environment and act on it.
(03:14):
So we kind of contextualize thatwith vehicles
and how it's kind of analyzingthe environment for people
that are just hearing the termartificial intelligence.
Can you kind of explainwhat it actually is?
No, it is basically that,you know, when we as humans,
when we see something, you classifywhatever you see.
When I see you, for example,if I've seen you before, I recognize you.
(03:38):
If I have not seen you before,then I say, oh, he looks like this person.
Or you attach a label to the new personthat you're seeing.
Even though you may classify it as closeas something you say, now I know him,
and I know that this is what his name isand this is how he looks.
And so all of the features that you havewith your name then get attached, right.
(04:01):
And so now if I start doing thisfor all the things that they encounter,
and that helps me in recognition,but also it helps me in things
that I have not seen before and be ableto classify to the nearest possible match.
And this kind of a way inwhich you are learning from the data.
So here we are talking about visionand recognition through vision.
(04:23):
But it doesn't have to be a vision,any data for that matter.
The more you're able to trainbased on the data that you have seen
and that's what you do as a human,then you use that for
not only understanding the dataor recognizing similar things patterns,
but you're also able to navigate to thingsthat you might not have seen before,
but you have a sense of whatit might be like based on that learning.
(04:47):
So in a sense, you can say AI is basically
a set of learning prediction
and therefore being able to make progressin terms of making decisions.
But it's mostly about how you're ableto take
the informationthat is at hand, process them,
and then build models based on thatso that learning
(05:10):
those models can help you navigatethrough new data on a similar data,
or even exactly that data in,depending on what the outcome is.
Whether you want to recognizeor find the closest thing that is to it,
or be able to build thingswith what you have learned
and be able to now build thingsbased on what you have learned.
So you said progress there,and I want to think about the future.
(05:31):
Can you tell us some of the thingsthat I could be used for?
Everything that you can imaginein the world around you, because it's like
asking the question, you know,but what can a human be useful for?
The question is no different, isn't it?
Once you have a person who is capable of
doing something, then you say thatthis person can do these things.
So you take any task,whether it is a driving task,
(05:54):
whether it is a task that is based on
predicting the weather or whether it isa task based on how do I learn,
what do I need to learn with thatwhich I don't know anything about?
You take any task for that matter.
You can always find a waythat I can be useful in that context,
because at the end of the day,it is all about data.
Learning from the data, building modelsand then using those models,
(06:16):
then being able to predictbased on what is not known yet.
So these are the kinds of thingsthat you do in every aspect of your life.
I mean, you look at your everyday activity
right from the time that you wake upuntil you go to sleep.
Every activity that you encounteris based on some kind of a recognition
pattern, recognitionas some kind of a decision that you make
or some kind of a learning that you havethat helps you for the future,
(06:38):
and some kind of setof some new hypotheses that you double
based on the informationthat you have gathered.
All of these things are what you doevery day in your life,
from morning to night.
One or more of these kinds of things.
So I, for me, is exactly similar to that.
It's now an agent instead of a humanwho is essentially facing
the same set of things. A lot of data.
(07:00):
There are existing models.
You build new models or refinethe models, use it for recognition
or using it for developingthe closest match or using it
for developing newer hypothesis
or helping us in making decisions.
So all of these things are what we doas humans,
(07:21):
and therefore I can do similar thingsin any field that you can think about.
Right? Is part of the challenge
then deciding what fieldsit's most beneficial to use it.
I mean.
It depends on which field where you needthe most help in, and particularly
in terms of being able to automatemore easily, if you could put it that way.
So the low hanging fruit opportunitiesmight be easier to build models
(07:43):
with a small amount of datastill be able to do meaningful things,
but that does not preclude usfrom building these mega models
that you hear about.
ChatGPT and others, which can be usefulfor a variety of other applications.
So at the end of the day,it is not limited,
but it depends on whatyou want to apply that for
and with what level of efficiencyand efficacy that you want that model to
(08:04):
work for you, or that particular solutionyou want to work for you.
And that's what it is based on.
And so if you take health,there are aspects of health
that can benefit from using AI inin all the way from understanding
the genome to all the way to deliveringpatient care, individualized personalized
patient care.
If you take health,if you take transportation,
you can have a lot of automationbuilt into the transportation.
(08:24):
You can have a lot of safety featuresbuilt because of the automation
that is there in transportation.
It might help in terms ofspeeding up things versus where they are
right now in terms of what you can do withexisting transportation infrastructures.
So it could be in transportation.
It could be in learning.
A student now can find thatthey can do anything in terms
of advancing their learning aspirationsby being able to find what are those
(08:47):
specific things that you findthat you don't understand.
Well, now, for example,if you're taking Calculus Course,
let's say that some of the calculus courserequires you
to have an understandingof mathematical concepts
that were in your priormathematics courses,
but some of which you are very familiarwith.
Some of thisyou find that you either did not learn
well or you've forgottenwhatever that might be.
(09:10):
And so what happens is,depending on what you might need,
you could go back and get the appropriatethings filled up
so they are able to understandnew concept them.
So essentiallyit becomes like your partner if you, me,
you and the machine in this caseAI are working together in terms
of being able to expand your capabilitiesor your effectiveness.
(09:31):
And so that'swhat makes it so interesting, exciting
in whatever field it isin, you can find something
that will enrich you, empoweryou, augment you,
make you a lot more effectiveand efficient.
There's a lot of fear with peopleand their job security
and how this might impact that.
(09:51):
Can you talk about some of the waysI seem to be able to be worked alongside?
How will it be a toolthat benefits workers everywhere?
That's a good question.
Again, it depends on the work.
Is such a broad thing right?
There are so many dimensionsand classifications
of the types of workin every type of work.
(10:12):
It doesn't matter whether you'rea physician or a physician assistant,
if you are a student,a tutor, or a professor,
if you are a truck driveror you're an automobile designer,
it doesn't matterwhat your type of workers.
You can always find those thingswhere the AI tools can be very helpful
to you to enhance your capabilitiesand abilities.
(10:36):
And I would say that the things
that you may not be doing right now,which you are capable of
because you're doing the thingsthat otherwise occupy you.
If you're unburdened by those things,
then you are able to now useyour skill sets even more,
and maybe your creative mindsetsare expanded even more,
(10:58):
and artistscan now do even more creative art.
A physician can do amazing diagnosis andtreatment beyond what they are right now.
All of these are possible because younow this becomes a companion for you.
So in a sense, when people worryabout all, is my job going to be lost?
It is not that you lose.
Your job necessarily is always the case.
(11:18):
Yes, in some casesthat might be the case, right?
A machine may be an AI devicemay be able to do that what you do.
But to methat frees you up to doing other things.
Now the pace of progressclearly is much faster than it was before.
I mean, you could arguethat similar thing was true in in
when tellers were thereand then the ATM machines came about is
(11:40):
so you could think of itthat those who are tellers,
the 50 of them in a bank now, maybethere's 1 or 2 with the ATM machine.
Let us say on what you doelectronically with your mobile device.
But those 48other people have found other ways
in which they are using the talentsand skills, not necessarily
only in the bank environment,but in other environments.
(12:00):
This is what will happen at AI too,is that the jobs of today,
some of them will be augmented,enriched, enhanced.
Some of them will be lost, in which case
people will naturally gravitateto learning those skill sets and mindsets
that they will expand to being ableto create the new jobs of the future,
or to do the new jobs of the future.
(12:20):
So it's very hard to say,by looking at only a static picture
of where we are and saying,what I am doing today, be
gone is a very narrow perspective throughwhich you look at the world, right?
It's much, much,much more broader than that.
Now comes with the associated questionsand things that we ought to do.
So there is no longer this feelingthat, you know, you had this
(12:42):
18 years of educationor 21 years of education
or 25 years of education,whatever that is that you do.
And then you go take a joband then you stay with the job.
And that was no longer true. Even,you know, a couple of decades ago.
And it's no more becomingless and less true now. Right?
So you
can imagine that for that,learning is a lifelong pursuit.
You're constantly upskilling, reskilling,retooling, learning new things.
(13:07):
So it is not like, you know, let me finishmy studies and then I have my work.
It's the learning and work and learninghave become so intertwined
that it is a symbiotic.
It's kind of an activity.
And in that kind of a scenario,nobody's outdated.
No skill becomes unnecessarybecause you have to acquire new skills,
(13:27):
so your skill becomes different.
It constantly shapes, modulates itselfand so on.
So that's somethingthat is very interesting
as a paradigm shift,if you want to look at it that way.
But what is even more excitingis that you can think of the kind of jobs
that you were doing that you felt like,why am I doing all of this
is now going to be replaced by machinesthat can do the job
(13:48):
that allows you to express your skill,ability, creativity,
and other kinds of abilitiesthat you have to be able
to express in its fullest form.
And we may not even know what they are.
It's some of these thingsmay not have even come out of you
because you've been anonymousabout boredom, but
you have been engaged in those activities
(14:09):
that have not let your creativitymanifest itself in its fullest form.
Human talent is capable of doing a lot,lot more than what we think.
So I think I want to look at itfrom that futuristic perspective,
because if you look at anything
which is empowered by technologywill be thought of as,
oh my God, this is going to take awaysomething from me.
(14:29):
But that is only by imagining yourselfas that
just static picture of today,but not envisioning what it could be.
The tomorrow.
So thinking about tomorrow,I want to ask you about how NSF
is working to kind of guidehow AI is developed in the United States.
Let the questions so clearly you said,what area is this?
(14:49):
I have an impact.
And I almost answered the question,missing everything and anything.
And you know what kind of impactit will have on humans.
We talked about the potential potentials,you know, amazing.
And and then it could be enormous.
We talked aboutwhat kind of things it will do for jobs,
answered by saying that it can empoweryou, enrich you, and so on.
(15:10):
So when you look at that,NSF is engaging every dimension
of all of this,all the way from looking at
if you need to have a device,the device starts with understanding what
the materials are to building the devices,to building the technology.
And you look at the physics of it,the chemistry of it,
(15:32):
the mathematics of it, and the engineeringof it, all of that and the design of it,
all of that is something that NSFmakes possible through its various tweaks.
Then you look at all the applicationsthat it can be put to use, whether it is,
a geoscience application like,you know, it could be exploring the planet
and finding solutions for challengeslike climate and mitigation,
(15:53):
climate adaptation and so on.
It could be that
or it could be a bioscience problemthat you're trying to understand
the fundamental basis of how the humanbody functions all the way from a cell.
Right.
And how might you personalize strategiesfor having a good health
outcome all the time?
A bioscience structure,it has those kinds of things.
How can you build better environments?
(16:13):
By having good understandingof biology and, and so on.
Then you look at our social,behavioral, economic scientist directorate
and all the work
that NSF does is at the end of the day,it is all about interfacing with the human
and all the aspectsthat come with the social, the behavioral,
the ethics, the policy,all of that is all centered
around the work that the socialeconomic sciences director works on.
(16:37):
And as I said, the mathematicalphysical center structure.
And if you look at applications
like astronomy, understandingwhere we came from, right.
Again, all of that and more iswhat NSF is constantly engaged in.
And when we talk about educatingthe future,
the talent of the future,
again, our education Directorateand all of the directorates are engaged
with educating, you know, helpingwith educating better K-12 students,
(16:59):
better undergraduate students, graduatestudents, better community college
students, skill sets that there areall these students build better research
and then all constantlyin all of these things,
pushing the boundaries of discovery,
okay.
And then providing the platformsthat allow all of this
to manifest itself in terms of betterindustries of the future,
(17:20):
that are entrepreneurial outcomes,better entrepreneurs, all of these things.
I mean, you couldI can go into every aspect of it.
You will find.
That's why NSF is a unique agency.
It touches pretty much every aspect
of what is needed for us to deliverthe futures that we are talking about.
As much as I as a field is important,
but I get touched by and influences
(17:43):
a host of other disciplines areas,and so therefore
it is a comprehensive picture,not just only what we do in the computing
informationsciences, engineering directorate,
but as you know, AI has been involvedfor the last several decades over.
Something like 50 years.
So 50, 50, 60 years,but also in the investing in AI Institute.
Right.
Which means that we are lookingat every possible avenue
(18:06):
by which I can be furtherpropelled into the future.
But what I can do to propel thingsinto the future,
these AI institutes are tremendousinvestments, right?
That's another thingthat that you have seen that we do here.
So if you look at the collectiveinvestments in AI around AI
and what NSF makes writ largein, in various,
(18:26):
fields of science, engineeringand technology, you find that all of
that means something for the advancementof this into the future.
For the final question today,I want to ask you about the future.
What about how AI is developing excitesyou the most?
I think what excites me the most is,you know, I've always believed
the human potential is enormous.
(18:46):
But yet if you look at globallyin our nation to but globally,
I don't think we have been ableto exercise all of the human ingenuity,
the innovative mindsetand the human spirit.
And so what I and present to usthe opportunity to be able
to unleash all of the human potentialfor the benefit of humanity and beyond,
(19:07):
and that, to me is very excitingthat we will be able to do that
at scale and at speed and everywhere,not limited to a few locations.
That really makes it very exciting.
Any minute. Imagine,let's take our nation.
Imagine every area,rural area, every urban area,
all the 50 states and territoriesof our country and people everywhere
(19:31):
being empowered, energized to be ableto exercise their talent to the fullest
without any holding back,without any constraints.
Imagine that that can do amazing thingsfor our nation, build
tremendous prosperity for our nation
and through that, and have prosperitypossible all across the globe.
Specialthanks to Doctor Sethuraman Panchanathan.
For The Discovery files, I'm Nate Pottker.
(19:52):
You can watch video versions
of these conversations on our YouTubechannel by searching @NSFscience.
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Discover how the U.S.
National Science Foundationis advancing research at NSF.gov.
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