This week on the Be Epic podcast, Brent sits down with Varun Grover, Distinguished Professor of Information Systems and George & Boyce Billingsley Endowed Chair in the Sam M. Walton College of Business at the University of Arkansas. They explore the profound effects of AI on individuals, businesses, and society. Varun highlights areas where AI can enhance lives through applications in healthcare, education, and more. However, he cautions that without understanding how AI reaches conclusions, trust remains elusive. Brent and Varun discuss improving AI through specialized models, explainability, richer inputs and contexts. Varun asserts that augmenting human skills rather than automation ensures AI uplifts roles and fuels innovation. Listeners gain insight into AI's trajectory and its role partnering with instead of replacing humans.
Podcast Episode
Episode Transcript
Varun Grover 0:00
You never have innovation through automation, you will only have innovation if there
are ways in which the AI and the technology can work together.
Brent Williams 0:12
Welcome to the Be Epic Podcast brought to you by the Sam M. Walton College of Business
at the University of Arkansas. I'm your host, Brent Williams. Together, we'll explore
the dynamic landscape of business and uncover the strategies, insights and stories
that drive business today. Well, today, I'm lucky to have with me Dr. Varun Grover,
and Dr. Grover's the George and Boyce Billingsley Endowed Chair and Distinguished
Professor of Information Systems in the Walton College. Varun, thank you for joining
me today.
Varun Grover 0:46
I'm delighted to be here. Thank you.
Brent Williams 0:48
Well, I think we're going to touch on some interesting topics today. But maybe before
we do that, I would love for our audience to get to know you better. I've gotten to
know you over the last five years, I think you've been at the Walton College since
2017. But tell us a little bit about you your background, and before coming to the
Walton College.
Varun Grover 1:09
Sure. So. So I'm a techie at heart in the sense that I did my undergraduate degree
in engineering, electrical engineering, and, and that's where my interest in technology
started coming about. And I had the good fortune of getting into a very competitive
engineering school that allowed me to think systematically about technology. So when
I, when I graduated, I kind of felt that I was in a very narrow silo, I understood
technology gyrations I understood, you know, technical concepts, but I didn't really
understand the world, the broader context. And so I decided to do an MBA and then
go into my PhD in information systems. And then after that, I've been basically in
academic jobs, I was at USC, and then I was at Clemson. And my primary goal or responsibility
in those jobs was to, to build the PhD program, and conduct research and set up centers,
and have some outreach, dealing with important problems in the Information Systems
domain. So I've been integrally involved in this in this field, and the field has
evolved, because the technology catalyst is kind of driving changes. So if you think
of technology in the 1960s, and 70s, and 80s 90s, and now, now we're in this digital
world, that is a lot of people really don't know how to deal with it, and companies
are still struggling. And now we've got AI coming in, that's adding a new wrinkle.
And so what I like about the field in general, is that we don't have a dearth of problems
to study, there are lots of challenges faced by by companies faced by individuals,
and now the impacts transcend to society. So there's a lot of really, really interesting
issues. And that keeps me excited about the field.
Brent Williams 3:18
Yeah. Well, you know, Varun, you're, you are a researcher, you're a teacher, you know,
you serve the University and the College through the normal mechanisms. But you have
spent the majority of your career really focused on research. And as you say, the
your work and PhD programs are really advancing the research of these institutions.
Just when you think about business research, maybe more generally, how, how does it
impact the world? How does it impact others? And how have you viewed that over time?
Varun Grover 3:53
Yeah, that's a really interesting question. Because even business research has has
evolved. If you think about in the 1950s, business research was not much of anything,
it was more, you know, go out maybe some outreach and case studies that you did with
with businesses, but then the scientific aspect of business research came in the 1960s,
1970s. And we kind of went overboard with the concepts, scientific concepts of theory
and became somewhat disassociated with practice. So my thinking is that in in a, in
an applied business discipline, it's really important that we create interventions
to practice. Any research that we do, should not be esoteric and isolated from the
real world. And so when we talk about when we think about research projects, traditionally
our approach would be, you know, look for research gaps in the literature and then
try and fill those gaps. I think the more productive approach is to To identify real
problems faced by practice, and then to convert those practical problems into research
problems. So it's more like engaged scholarship, you engage with practice at a deeper
level, and try and look at their problems, and then translate those problems into
research problems so that you can create interventions, as a result of your research
that actually make a difference in practice.
Brent Williams 5:27
That's right, what a good way to explain that I just couldn't agree more about at
least in my own research that that's where the research questions that I've been most
interested in then that ultimately, I think the research that has the biggest impact
for my own career comes from same for you.
Varun Grover 5:44
Absolutely
Brent Williams 5:45
Well, I'll, I'll brag on you a minute, because I don't think you would brag on yourself
in this way. A couple of the interesting statistics about you as a researcher, I think
you've published more than 400 articles in your career, over 100 in the top journals,
and you are listed as one of the 100 most cited, not just information systems, but
business researchers in the world, what an accomplishment. And so you have truly over
a career made a real impact in the academy and in practice.
Varun Grover 6:24
Thank you. Thank you Brent.
Brent Williams 6:26
Well your research, yeah, I told you this before, I'm kind of borrowing from your
bio here. But you know, at a real broad level, you know, as I saw it described focuses
on the effects of digitization on individuals on businesses and organizations and
even society, tell us a little more of a high level before we get into some of the
more detailed questions.
Varun Grover 6:50
So I've always been interested in how technology creates value. And information technology
is particularly of interest to me. So when we look at computing technologies, in a
business context, what are the aspects of these technologies? And the and the context
around them, that lead to value value at the individual level? So how can technology
enhance individual productivity? How the value at the group level, value at the organizational
level? How do you actually create competitive advantage through these technologies?
How do you create greater profitability, more revenues? And then at the market level?
And that is how do you make markets more efficient through technology. And then now
even more so at the societal level. So with technology, and particularly digital technologies,
penetrating every aspect of what we do, there are issues of value, and the value could
be positive or negative. So now we are seeing you know, this, there's two phases of
technology, you have the intended consequences to create positive value for companies.
And then you have sometimes unintended consequences that create problems for society.
So I like to study at every one of these levels, I like to study different questions
regarding the value proposition of digital technologies.
Brent Williams 8:27
Well, this has to bring probably to anyone's mind, you know, when you talk about the
enter the the intersection of society and digital technology, as we sit here today,
the conversation around artificial intelligence, AI, is everywhere. All right, you
know when chat GPT popped on the scene, it really just brought it to the consciousness,
even though this has been developing for years, if not decades, it really brought
it to the consciousness. You're not an AI researcher, I know. But but I do think that
you're probably in a unique position to think about this intersection between this
technology, and people, businesses and society. So maybe just I'll just I'll stop
right there and kind of say, all right, as you're observing this phenomenon of AI,
what's kind of going through your mind and what are the observations and questions
that that are becoming apparent to you?
Varun Grover 9:28
Yeah. As many people are fascinated by AI, so am I, as it's not, it's not my primary
area of research, but clearly it falls within the domain of digital technologies.
And like you said, ChatGPT came on the scene and it blew away people because not because
it wasn't necessarily the most accurate or more most interesting output, but it gave
people the sense of the possible if this is the starting point, and it can generate
such interesting outputs, then where is the world going. And I remember the first
time I played around with GPT, I asked it to write a story, I said, you know, here's
a little girl walking up a hill, and there's a, there's a big rock at the top of the
hill, write a story for a five year old. And it wrote a pretty compelling story that
I actually read to a five year old. And then I said, okay, redo the story for a 10
year old and it reformulated the story, change the language, and again, fairly compelling
story for a 10 year old, then I asked her to do one for an adult, and it did it. And
then I pushed it to be more creative, in the ending, make it more interesting, make
it more controversial, make it more enigmatic. And every time I did it, it came up
with interesting changes. And I said, wow, you know, this is actually doing it from
the corpus of data it's been trained on, and yet, it has this layer of creativity
that we don't quite understand how it's getting there. And this was the early version
of ChatGPT. More recently, I was playing around with now there's many, many of these
chatbots, AI based chatbots. And I noticed one of the differences. So I was playing
around with a chatbot called Pie, which now I believe has been acquired by Microsoft.
And it I found it to be extremely conversational. And what was different about the
earlier versions in this version is that it had empathy. It actually could relate
to me, and it was taking cues from my text, and putting that emotional response back
to me.
Brent Williams 11:56
Interesting.
Varun Grover 11:56
And that was very interesting. So it's like, it's almost like we're coding emotions
into these training sets. Now, to provide far more conversational far more personal
AI, personal intelligence reactions. And I stopped thinking about this, it is absolutely
fascinating where this can go. Because if you can have that level of interactivity,
and conversational and empathy with with individuals, you can put this front end on
pretty much any intelligence, and you can access the world. So customer interfaces,
so providing excellent customer service, that make it make it conversational, so you
can align with what the customer wants. Personal learning it for in the education
sphere, companionship for old people, you know, if you actually put a physical manifestation,
like a robot, and then you have this AI back back end, you can you can maybe alleviate
some loneliness in the world. And then so clearly, there's this tremendous implications.
And then you see the applications of AI. Some of the things that we've often talked
about is in the medical field, in the medical field, you can feed in radiology reports,
and it can find abnormalities, that's kind of low hanging fruit. But then when you
have this kind of conversational AI, you can put that in front of medical knowledge
and you have a doc in a box. And when you are considered the shortage of primary care
physicians, this would be a very interesting way to layer our medical interactions,
where you have the first layer of interaction where you can feel comfortable talking,
even though it's not a person, you can talk talk to the box, and that can do a diagnosis
and write up a report. And then it can go to the next level for verification with
a human and then go to the next level for a referral to a specialization. It can change
the medical field quite quite significantly. And we've seen applications of AI in
supply chain and manufacturing with robotics, in the financial sector with wealth
planning. And so when you have these interfaces, you basically can interact and get
it more aligned with what you want in terms of your wealth goals. And, and recently,
I was reading an article that described IBM's Watson that was working with a fashion
house, and they actually trained the AI on videos of models walking down runways,
and social media included, and the AI was quite adept at identifying fashion trends,
and new fashion clothing designs, based on what they observed through this corpus
of data. So it's remarkable at the diversity of areas that AI can have have an impact,
which is why it's so profound. And in some sense, also a little scary.
Interesting. Well, you mentioned profound and scary. You also, you talked about a wide potential set of potential impacts on people business society at large. I'll, I'll kind of dive into two and ask you a question. So you mentioned wealth management, and you mentioned health care. And as I was sitting here listening, I was thinking, oh, those are two pretty personal topics to me, like, one, you know, how am I managing my money and two managing my health? And I know that I think that a lot of your research in digitization and how it affects people in organizations and society looked at least at trust at some level. So what have you learned about the way that trust, I don't know, I don't know if it mediates those two. Tell me what you've learned and how you think that that's going to play out with AI?
Yeah, this is a really interesting question of trust. Because historically, when we looked at information systems, we programmed these systems, we could actually code them, we said, so if we had a, we had to make a computer play chess, we'd say, okay, this is the way the queen moves. This is the way the bishop moves. These are the rules of chess, we put it into a program, and then it will play chess. So the earlier AI systems were actually programmed systems, they will rule based systems. So there was AI in the 1970s. But it was usually referred to as a snake oil of the 1970s and 80s. Because it over promised and under delivered, you could only have some gaming applications. So yes, you had medical diagnostic systems, but they were based on rules, the rules were coded in. So if a patient had temperature over 98.6, the patient had fever, if their white blood cell was over a certain level they had an infection, and it would, you'd kind of give it the symptoms, and it will pull out the rules and come up with the diagnosis. What's changed now is we have had the perfect storm of processing power. With these GPUs, graphic processing units, Navidia is the big player there, we have massive amounts of data, and low cost of data storage. And we have with this process of processing power and data, we can kind of look for patterns in the data that are and, and identify these patterns and make predictions based on these patterns. So the large language models that we see of today, are really next word predictors, if they although now, we can actually feed them videos and images. So they're multimodal models. But the language models are, really next word predictor, so they don't have any raw intelligence of their own. But they're kind of looking for these patterns in the data. And so while the old systems were programmed, the newer systems are digging out these patterns, almost like your neurological connections in the brain. It's actually creating these connections. But we don't really understand how they're creating these connections. It's almost like asking a person, say, How did you come up with that decision? Yeah, and what what neurons were connected in your brain to come up with that decision. So we don't really understand it. And so if we don't fully understand it, it's like Warren Buffett says, Never invest in anything you don't understand. So here, we are relying on the outputs. And we don't really understand how the outputs came to into existence. It's massive amounts of data, millions of parameters in the model. And so without understanding it, why should we trust it? And if we can't trust it, then why should we use it? And so I think it's a fundamental question of technology and value, which is my major stream, that we need to be able to trust these systems. Because another way to think about this is typically, if you look at scientific research, the idea of actually looking at patterns from data was considered voodoo science for many years.
Brent Williams 19:51
Yeah
Varun Grover 19:51
This induction you don't look for patterns.
Brent Williams 19:54
Yeah
Varun Grover 19:54
You come up with a theory or a statement and then you go on to get data and test it.
Brent Williams 19:59
Yeah, that's right.
Varun Grover 20:00
But what are large language models doing? What is AI doing? It is really creating
and testing theory simultaneously without any scientific underlay. So when I think
about this, I think, you know, I've always grown up through my information systems
career, thinking about data, information and knowledge as a trichotomy. So you have
data, which are raw facts. And then you put the data together, and you get information
which informs you. So you get interesting reports that reduce uncertainty or informs
you. And then when you apply it, and you use organize the information, with experience,
you get knowledge. What we've done with AI, is we've removed the middle layer, we've
gone data is knowledge. And so we're basically taking massive amounts of data about
the world. And we are proclaiming that this is actually creating knowledge. So we
don't really need to understand the world to know it. It's like we're just take, so
that layer has gone. So that's why this idea of trust is so fundamental, because we
don't really understand how these connections are made. And so I I was thinking about,
you know, as you why do people trust other people?
Yeah. So it's just thinking exactly the same question.
And so so and do the others elements actually translate to AI. And I think people trust other people, because they may have a connection with them, they actually relate to them, they have empathy, or they relate at some subliminal level. So they say I trust you. People may trust people, because they understand where they're coming from. So if they make a decision, if you talk to someone, they make a decision, you can ask them. Okay, what's the basis of this decision? And do I trust you to explain to me how you came up with this decision. People trust people, because they are predictable, they are reliable, they may give accurate outputs. So if someone is erratic, we probably won't trust them. But if someone is more stable and predictable, and comes up with good, good outputs, we tend to trust them. And people trust people because we might share the same social experiences. So I think these elements of we trust people, because we can connect with them or relate to them. We trust people because we can understand where they're coming from, we can ask them, we trust people because they're reliable and not erratic. And we trust people, because we have a shared social world, is generally why people trust people. These elements are not very prominent in AI. And so, so I think that's why trust is a kind of an important issue. Although they're changing.
Brent Williams 21:30
Well, that's what I was gonna ask you, you know, how do you see that changing and
evolving? The one that I could see that you just mentioned. Well, the model is getting
more reliable, let's say less erratic. That one may change, you know, but and you
even mentioned the ability of, of maybe I don't think you said it this way. I think
you were mentioning Pie, the the product you were working with, that it had a feel
of empathy, which might mean you could emotionally connect to it more easily. So some
of these things seem to be evolving,
Varun Grover 23:44
I think so I think that from whatever little observation I've had over the last year
and a half, it's not been that long since ChatGPT was removed, released. But this
idea, I can see improvement in empathy, I think that element is there. So we are starting
in on that trajectory in terms of understandability, there is a whole field or subfield
of AI. That is called Explainable AI. How do you get take this neural connections?
And how do you actually convert it into a language that humans can understand so they
can explain the rationale for their decisions? Now, it may not be that important for
radiology reports. But if we're dealing with a business discipline, you know, often
you want to understand the rationale for the decision. So Explainable AI is actually
being worked on. And and it's important, and it's it's particularly important because
AI is all about data and connections and patterns. AI is not particularly good at
reasoning. So if we tell an AI that Look, if they're looking at a pattern, x is the
parent of y, it will not naturally infer from there or deduce from there, that y is
the child of x. That reasoning ability will only come about if it's in the corpus
of data. So the question then becomes, you know, how do you get this reasoning out
of the AI, and that's where a lot of the research on Explainable AI is, give you another
example. We can put millions of chess games played by world chess champions and feed
it into the AI and it'll figure out the rules of chess and what works and what doesn't
work. So that it will basically be a really good chess player. But if we change the
board from an eight by eight board to a nine by nine board, then the training doesn't
really help because it needs more than that it needs some kind of reasoning ability
to play chess well on a nine by nine input. So I think that that idea of how do you
get that reasoning to come into you know, the box or the corpus of intelligence of
AI is is something that is being worked on through Explainable AI. So empathy? Yes,
we're seeing improvement there. Explainable AI, we're not quite there. But there's
certainly a direction there. And like you said, absolutely the corpus of training
data. So most of the models, we had an AI foundational models, like from open AI.
And from Google and Microsoft, these models are trained on massive corpus of data
from the internet, the internet data is noisy. And it is, you know, it has a lot of
a lot of problems, which means it can be a pretty good general, it can provide general
advice on a lot of things. But if you're looking for very specialized advice, sometimes
these models hallucinate and create these arbitrary outcomes, because they're not
fully trained in those specialized areas. So I think one of the trends is how do you
actually curate data for training and make these models more accurate and better?
And how do you move away from the big tech companies which have the resources to train
these large foundational models, to smaller models that are done through open source
where you can, individual companies may not have to invest millions of dollars in
training these models, but they can train them on their own data,
Brent Williams 27:49
Right.
Varun Grover 27:50
And, and become, it can become more more personalized and more accurate within that
domain. So I think that's the third component of trust, where these models are being
trained on better data. And sometimes even instead of the large models, we're getting
smaller and more specialized models that are more accurate. And I think that's a trajectory
that will improve trust. And finally, in terms of context. So I think that context
is also improving, because the earlier models had very little that you could put into
the model in terms of input. Now, some of the newer models, you can put entire books
and and say, you know, so your context window, the number of tokens that go into the
context is becoming larger and larger, which means you can provide a richer context
to your decision making. And then you can have conversations to get the alignment
of get what you want from the AI. And so that alignment can continue by probing and
interacting with the AI. So I think on the third aspect, the context aspect, we're
also seeing improvements. So I will trust the box, because I can connect with it.
I can trust the box, because I know where it's coming from. It can explain its reasoning
to me, I can trust the box. Because it gives me good outputs, accurate outputs, and
I can trust the box because it's grounded in my world. And so all those aspects are
advancing. And so I think that on the technological front, we are seeing a trajectory
that's positive for trust.
Brent Williams 29:47
I'm gonna I'm gonna ask you about to human. Maybe let's start in the business context,
right. So let's say the models are improving, they're becoming more trustworthy. They're
certainly becoming more commonplace. They're still probably some of the like, there's
the excitement. There's some of those some people who think it's scary. Probably each
of us land somewhere on that continuum differently. But in the business world, you
know, the role of the human in business, does it, change it slightly? Does it change?
Fundamentally? You have any thoughts to share on that?
Varun Grover 30:28
Yeah I think that's, that's, that's a million dollar question, or maybe a billion
dollar question. or now given, given the market of AI, maybe a trillion question.
Brent Williams 30:39
If you can answer this, we can raise a lot of money for the Walton College.
Varun Grover 30:43
But I think I so, how should AI, so if if we are using AI, to automate jobs, if we're
using AI to, to automate and replace jobs, so you can, you know, you can do an accounting
job better, or you can do a legal job better. Then what is essentially happening is
we are devaluing labor. We're devaluing human skills and labor, because we're essentially
replacing labor with the technology. And that's going to bring the cost of labor down.
So substitution of jobs with technology is probably not the most innovative direction,
it's probably appropriate. And probably the incentives for corporations to make money
are in the cost cutting sphere. So we're certainly going to see a lot of that. But
I think that where we're going to see the human and, and the technology work better
is through augmentation. You never have innovation through automation, you will only
have innovation, if there are ways in which the AI and the technology can work together.
So if you automate a lot, you're basically devaluing the human if you augment you're
up valuing the human, because when you when you automate, your competition is between
the AI and the human, when you augment your competition is between the human with
the AI, and the human without the AI.
Brent Williams 32:24
Right.
Varun Grover 32:25
And so you're upskilling. So I think that we are at a point of inflection, where we
are making 1000s of decisions regarding AI. And it's really important that if corporations
are going to invest in AI, they should consciously think about how to augment human
skills through AI, rather than only automation. Because automation will probably give
you the short term bang for the buck in terms of reduction in costs. But the longer
term impact on innovation will only take place if you give people the discretion to
leverage AI and become and use it innovatively. So if there's there's a series of
tasks in your job, and there's a few that can be automated, that's fine. But then
think about what are the tasks in your jobs that can be augmented. That's where the
innovation comes in. And that's where the value is created.
Brent Williams 33:27
Well, Varun, thank you. Thank you for sharing your knowledge and that knowledge that
comes from decades of work and research and I know I learned a lot and I'm sure our
audience did as well. So thank you for joining me today.
Varun Grover 33:44
Thank you, Brent for having me.
Brent Williams 33:45
On behalf of the Walton College thank you for joining us for this captivating conversation.
To stay connected and never miss an episode, simply search for Be Epic on your preferred
podcast service.
