Rama Akkiraju, an IBM Fellow, IBM Academy Member, IBM Master Inventor, and Director of IBM’s Watson Division, is a pioneer in applying AI to solve real world problems.
She sits down with Cindy Moehring to discuss Akkiraju’s mission of “enabling natural, personal and compassionate conversations between computers and humans.”
Podcast
Resources From the Episode
- Fueling Possibilities: Welcome to the Positivity Pump - Chevrolet
- Couriers: Build Skills with Online Courses from Top Institutions
- arXiv: Free Distribution Service for Scholarly Articles
Episode Transcript
Cindy Moehring 0:03
Hi, everyone. I'm Cindy Moehring, the founder and Executive Chair of the Business
Integrity Leadership Initiative at the Sam M. Walton College of Business, and this
is theBIS, the Business Integrity School podcast. Here we talk about applying ethics,
integrity and courageous leadership in business, education, and most importantly,
your life today. I've had nearly 30 years of real world experience as a senior executive.
So if you're looking for practical tips from a business pro who's been there, then
this is the podcast for you. Welcome. Let's get started.
Hi, everybody, and welcome back to another episode of theBIS, the Business Integrity School. And today we have a very special guest with us as we continue season five, where we're talking about all things tech ethics, we have from IBM, Rama Akkiraju. Hi, Rama, how are you?
Rama Akkiraju 0:58
Hi, Cindy. It's a pleasure to be on your podcast. I'm doing very well. Thank you.
And happy new year to you.
Cindy Moehring 1:03
Happy New Year. It's wonderful, and it's wonderful to see you. I'm going to tell the
audience a little bit about you and then we're going to jump right into the conversation
because you have a lot to share with us in the work that you're doing. We're really
fortunate to have this time with Rama today. She's an IBM Fellow, a Master Inventor,
and IBM Academy Member and a Director of IBM's Watson Division, where she leads the
AI ops team with a mission to scale AI for enterprises. Now, let me tell you previously,
she also led the AI mission of enabling, this is really cool, natural, personalized
and compassionate conversations between computers and humans. And we're going to get
into all of that as we dive further into the conversation. Let me tell you also a
little bit about some of the awards that Rama has won. And then I'm going to have
you tell us Rama, how you got into this career and ended up where you are. But Rama
was one of Forbes Top 20 Women in AI research in 2017. She was featured in the A team
in AI by Fortune in July 2018. And she was named as a Top 10 Pioneering Woman in AI
and Machine Learning by Enterprise Management 360. Those are incredible achievements.
And I really think the audience would love to hear, Rama, how did you get from kind
of where you were maybe when you started your career to where you are today, which
is, well, congratulations. That's an incredible career you've had.
Rama Akkiraju 2:34
Thank you, Cindy, that's very kind of you. Well, you don't really plan for such things.
You just keep doing your work. And, you know, if you're doing good work, hopefully
things will get recognized along the way. And, you know, that's how they came about.
It's not like I had any specific plan that, you know, four years from now, five years
from now, I'll get there. But I would say Well, first of all, much of my career has
been at IBM, all through. And over the years, I have worked on a lot of products,
a lot of things related to applying AI to solving real world problems. So What really
excited me along the way has always been not just science or technology for the sake
of technology, but really grounded in solving real world problems. And that ranged
over the years from any number of things from looking at optimizing supply chains
back in the late 90s, early 2000s. Different kinds of analytics along the way, all
the way to applying AI to more unstructured data, which is the work that I did at
Watson, which led to some of the work that you referenced related to understanding
and modeling people better. But the theme has always been around something around
applying AI and different kinds of AI techniques for solving real world problems.
I would say maybe over the years that kind of built up and lead up to you know, the
work that I had done at Watson along the way, and I had always kept my publication
record because I kind of worked at IBM Research for several number of years. And that's
the culture that you grew up in where you you publish, right, and that also keeps
your aperture broad and wide and open to the things that are happening. So that's
how it all came about, I would say.
Cindy Moehring 4:41
So one of the projects that you led was the AI mission of enabling natural, personal
and compassionate conversations between computers and humans. I find that fascinating
to think about machines being able to communicate with humans in that way. But I have
to ask you, computers as we know, they don't have feelings the way humans do, right?
I mean, they don't, they don't feel, they don't have that same level of empathy that
is just natural for humans. And they aren't human. So how is it that you can work
on a project to make computers have compassionate conversations with humans? How can
they do that?
Rama Akkiraju 5:26
Okay, well, good question. You're absolutely right. Computers cannot really empathize
with humans. And that's why I stayed away from calling them empathetic systems, compassionate
compensation system. Compassion is really about understanding what the other person
is feeling. And it is first detecting, recognizing and understanding. So if we want
to make computers really, so first of all, let's actually step back and ask ourselves,
why do we want to have computers are any agent to understand the emotional aspects
of people? Why not just be very cut and dry? You know, they call for a problem? They're
asking for a particular answer for a particular question. Just provide that and, you
know, stick to that. Yes, there are use cases and you know, business scenarios where
that's the most effective way of carrying on a conversation or, or providing a service
to the human on the other side. You know, let's take an example and ground this, this
particular conversation. Let's take a chatbot, which is one of the prominent applications
of AI that's coming up. Increasingly, companies are deploying chatbots for customer
support types of scenarios in in narrow and specific domains where they believe that
they can have success and really provide effective responses to customers and solve
their problems. Not it's not something ready for every particular kind of a problem
that customers of any company might ever have. But you know, narrow, narrow, more
frequently asked to type of questions and such customers are increasingly deploying
those types of chatbots. So if we, if we take that as an example domain, I'd say,
why would you want a chat bot to understand the emotions and feelings and sentiments
of a particular customer? Well, first of all, it's, we as humans, really love to be
understood. When we are calling about a problem, you know, when you're angry about
something, you know, if the voice of the person if the human on the other side is
actually calming. And they show some kind of an understanding of your problem and
say, Oh, I'm sorry to hear that you're having such a problem. Let's see if we can
help you even if that is a phrase that they use often. And it's a pre coined phrase
that they're using, you still feel a little better because you feel that they're listening
to you that calms you down. So it kind of is, you know, the idea of building compassionate
chat bots kind of originated from it. But then now after the after this I'll extrapolated
to other use cases, we're actually having such compassion might be a lot more useful
and important and relevant. In a chatbot type of conversation, if you understand if
a person is calling really angrily natured about something, if the agent, even if
even though it's a chatbot, if it said, I'm sorry to hear that you're having that
problem, I see that you're frustrated about it, I will do everything in my power to
help you with the problem. And if it actually solved the problem, right there even
it gives you that little bit of time to go look for the right answer, because the
user now felt like, you know, they've been heard. And we've done a bunch of studies
to better understand if that is indeed the case, based on the the tone that the user
is coming with and the tone that they're expressing, if you modify the response, or
if you append the response with these kinds of phrases that demonstrate their particular
some kind of understanding of their particular emotional state, they actually go back
more satisfied than they were to, if they were to just simply receive a response with
no acknowledgement of their emotion.
Cindy Moehring 9:25
Hmm, interesting.
Rama Akkiraju 9:26
That's actually it's proven with studies and that's one of the use cases that we applied
it to, but there are many other use cases where you would want to have some kind of
an understanding of the user's emotional state. The robots or bots have been built
for as companions for elderly people or people who are lonely. There are situations
you can imagine. If a bot is exhibiting some level of maturity and understanding of
after particular render elderly person's emotional state and depending on that, if
it engaged in a conversation, say, if the person is being very moody or not chatty,
maybe you know the system will get that and from the conversation, maybe read a joke
for the person or crack a joke, to lighten up the mood or, you know, read an article
that they might be interested in. So things like that there, it's been used for those
kinds of scenarios. So yeah, understanding in that situation, you can imagine, having
a better context around how the person is feeling is is very important for the bot
to have me engaged in a meaningful emotional support, provide that kind of emotional
support for the person.
Cindy Moehring 10:43
I have to say, I think the chat boxes while they're getting better, my own personal
experience, they still have a little ways to go, if I'm really upset about something
and I call, you know, to talk to somebody in customer service, I will oftentimes get
very frustrated by the questions that the Chatbot needs to ask in order to kind of
figure things out and feel as though I would just do better if I talk to a human.
You know, it will frequently say, let me talk to a human, let me talk to a human which,
which they're getting better about, kind of getting to that faster, as opposed to
making you answer a number of nonsensical questions, that when you're in an upset
state don't make a lot of sense, I often find but I will say something I've gotten
better. And I have had a few problems resolved without having to resort to saying
speak to a human.
Rama Akkiraju 11:30
That's really a design of the Chatbot system has to do with really the rest of the
comprehension, the intent understanding and response generation that chat that AI
can power behind the scenes, that technology part of it. And then there is the actual
the business decisions and the business process aspect of chatbot, which, you know,
company might choose to, for whatever reasons that they would want the only the chat
bot to take up certain kinds of questions and make it as hard as possible to reach
humans. And that's an unfortunate decision. But I really agree. You know, when you
do a lot better in terms of offering superior customer service to your customers.
If customers really want to talk to a customer, you know, a human just give them an
indication that, you know, we're happy to connect you with a customer, but the wait
time is going to be such in such sure to talk to this chatbot which may ask you a
few questions, but we may be able to help you, it may be able to help you or or it
may even they may even give some kind of based on, you know, start an average a statistical
answer saying, in the past, these kind of questions have been answered by chat bots
more effectively or within a matter of minutes has come as compared to, you know,
the wait times that you may have to have them to get to connect it to a human, which
might be you know, 20 minutes from now, whatever, you know, providing more information
to the users and making it more transparent would be a good business process designed
for a chatbot but I think that's more sort of the design aspects of a chat bot process
as opposed to the technology and the AI that we had to popularize the courtyard setting
other questions,
Cindy Moehring 13:13
So Rama on the idea of companions, especially for older people that you mentioned,
who may be lonely, do you see any you know, ethical issues or you know, issues of
integrity with encouraging relationships, if you will, between humans and computers
as opposed to other humans?
Rama Akkiraju 13:36
Yeah, well, very good question. There are all kinds of ethical questions to be discussed
and considered in building such kinds of tools. You know, obviously, if you can have
a human be a companion to an elderly person, such an arrangement can be made relatively
easily and inexpensively and on demand, right. But in many cases, especially in in
old countries like Japan, where there is a lot of older population, the percentage
of older population as compared to the younger is higher. Loneliness and especially
again, if the number of children or families and you know how the family structure
is joint families versus preparing themselves in such a lot of these social factors
play into a role that contribute to whether somebody would end up you know, alone
at an old age and in some situations like that where it is it may not be possible
and not on demand. If not for you know, every particular thing I would say there might
be some things that are very helpful for people who whose eyesight is not that great.
Yeah, having having a you know, bot readout newspaper article highlights is a good
thing. Why not? Right? It may not have so much to do with, you know, ethics and emotional
support, in that particular case. We already have, you know, all kinds of devices
in our homes these days Siri's and Google homes and Amazon Alexa, that listen to our
conversations often and provide various kinds of answers to our questions. So it goes,
I guess, depending on the scenario, and again, the use case, and I've seen, you know,
as early back as seven, late 70s, there has been work done to embed some of the the
compassion and understanding into embodied bots, if you will, you know, these are
like pets, chips and AI chips embedded inside them that provide, you know, comfort,
simple, you know, it's not even conversations, but they just simple commands and simply,
but,
Cindy Moehring 16:17
Almost assistants as opposed to companions. Assisstants,
Rama Akkiraju 16:21
Well, not even an assistant, they don't even do anything special. It's just that you
find a lot of comfort in putting them in a specific place. And I've seen these elderly
homes, buy some of those things, and people who are recovering from different kinds
of physical and mental illnesses who are experiencing loneliness, may find comfort
in having such pet like things. But I don't want to particularly take this conversation
in that direction, either. Right. I mean, it's just one of the use cases. And as we
increasingly, you know, we're already seeing in our daily homes, as we just spoke
about Alexa sensories and Google homes, you know, there is a lot to learn about understanding
the speech, natural language, understanding the accents associated with people speech,
quickly translating that, and, and based on the intent retrieving the right kind of
response and getting back to you in a timely manner. To help you quickly answer the
question that you have at hand, those are already there in our lives. And there are
a lot of stuff that's happening behind the scenes that we don't pay attention to all
of this natural language, understanding speech, understanding accent, understanding
that's going behind the scenes, and that we all find it tremendously useful already
in our day to day lives. So whether or not it's compassionate yet, but an understanding
is happening already. And there is a lot of AI that power set. Compassionate conversation,
understanding has, you know, a subset of these use cases where it would be helpful.
And we have to, of course, navigate this area with responsibility and ethical manner.
So we don't make people dependent on things. But if it can offer timely support, sure,
provides such a support.
Cindy Moehring 18:23
Yeah. So let's switch gears for a minute and talk about another responsible use of
AI that I think companies need to be thinking more about and employing. Where I also
know you've done some work, teaching AI to speak multiple languages. Throwing out
a big word here for the non technical audience. I think you call that polyglot AI,
where it can actually speak multiple languages. Why do you think that is important
for a company's responsible use of AI?
Rama Akkiraju 18:55
Yeah. Imagine a system, an AI system, let's say, let's take, actually these devices
that we use in our homes, Siri, or Google Home. Imagine if it can only let's say that
it is marketed and it's in the United States and somebody in the US bought it. But
that person is not a native US born person speaks English like me with Indian accent,
let's say, what if that device has not been trained well, to understand the Indian
accent of English. That device is pretty useless for me. First of all, its usability
is restricted because it only understands the the language and the accent spoken by
Native people who are born with no specific way. And second, it actually could be
Discrimination, right. And this has come up in different contexts where, whether it
is based on language, or based on accent, or based on image recognitions, you know
the word there is the color, skin color and other kinds of factors, it could lead
to, first of all reduced target market base for your customers, if you are a company
that's building, you know, these AI products, right? Secondly, it could be, it could
lead to all kinds of lawsuits for you, because you're, you know, in a way, consciously
or unconsciously, you haven't taken into account that there are people who speak,
you know, a particular language in different accents. And I'm only talking about English
right now with accents. But, you know, if it doesn't speak in different languages,
then of course, you are restricting yourselves to English market alone. So you want
the broader applicability of the products that you are building, you know, and bigger
reach broader reach, obviously, you want to them to cater to the different kinds of
languages that people in those countries and regions speak. That's number one. So
it's pure revenue and business case, you could argue. But beyond that, if you want
to really serve the community and the your user base, well, you want to cater to all
of the different kinds of variations that there that they have the context around
that includes languages, that includes accent that includes even industry specific
languages, because if you're going if you just take English, the kind of vocabulary
that spoken and used more frequently in financial services domain, insurance domain,
versus, you know, retail domain is so very, very different. If you if you take a speech
or text recognition system and expect it to do well, in an order taking, say, a service
that a fast food restaurant wants to deploy, it may not do very well, because it it
may not really recognize some of the new names that the fast food restaurant may have
given to its foods. Every drink that Starbucks creates right? Now, it hasn't been
pronounced before, because they concocted it, with a new name, and it doesn't know
how to understand such things. So AI, you know, if you want to really put it to use
in a particular problem domain, you want to really clearly first define what is the
problem domain in which you're expecting it to excel and be good at correct and make
sure that you have trained it, and you have tuned it to really do well in the domain.
An ideal world, we would have a good speech recognition system that out of the box
comes with ability to recognize any kind of language any kind of accent in any kind
of industry in any kind of region and all that. But that's solving general AI. That
solved that that is a much harder problem to solve. That would require too much data,
too much training, too much compute, too much time, too many resources.
Cindy Moehring 23:35
Right. That's what I was wondering, right?
Rama Akkiraju 23:37
Well, what right now where we are at is, you know, define your problem. Got your understanding
of where you want it to be good at, create your test. Data sets accordingly. Make
sure that when you built it, you test it on those domains and then bring it in. It's
a very narrow, special purposed system that works for that domain. But who cares?
It was a domain, it solves the problem. Yeah, that's the language. So the understanding
when I did the language work at Watson was that you don't have to really try to solve
the full general purpose AI problem to really solve the problem at hand you focus
on the problem at hand define the scope, the domain and the parameters that it needs
to do well at and then go get the corresponding data if you if you need the data to
train it or the kind of system that you're building and make it really good at that
and in doing so, be conscious of all of the the biases that you may, unconsciously
put in the system, ensure that you have a good test set and a test base and if need
be, hire third party, you know, testing company with different unseen data sets to
test and evaluate and give feedback on before you deploy it so you can avoid some
of the backlashes and pitfalls and those sort of things that may come about.
Cindy Moehring 25:15
Well, let's talk about social media for a minute, one of those areas where I think
the horse is already out of the barn, when you talk about, you know, multiple languages
and AI's being able to understand and actually companies use AI with multiple languages.
I mean, just take, for example, was in the news recently was the Facebook files and
how, you know, you've got Facebook in, in all over the world now, and in certain countries
where it's being used by, you know, some bad actors to promote things like sex trafficking,
and human trafficking and forced labor. And it would seem to me that that would be
a place where a company, tech company like Facebook should be able to use in their
investigation, like multiple language AI to better understand where that's happening
and get their arms around it. But that also sounds like it's this not a specific use
case, it's like something that's already out there. And you'd have to solve the whole
big AI issue in order to get your arms around a problem that's, you know, that big
for a company, is it? Did I describe that right? Or is there a different way that
you think of a company that may have some bad actors like allegedly Facebook does,
from the Facebook files, with AI, with, with their site being used in countries wrongly?
Is there a different way, they could use polyglot AI or multi language AI to get their
arms around that problem?
Rama Akkiraju 26:46
But I mean, you can always use technology for good or bad, there will always be bad
actors. And you know, this, this goes from times immemorial, you could use electricity
to light up a city or electrocute somebody, you could use nuclear power to light up
a city or to you know, bomb a city or country. So, so but we have bad actors. Yes.
So how can we stay ahead of it, right. That's really the core of the question, what
are the so just the way you're using AI to solve some of the problems at hand, bad
actors are also using AI in different ways to do bad things. So yeah, you have to
be constantly mindful and watchful and build the right kinds of checks and balances
and, and mechanisms to monitor how your product is being used. Put the right kinds
of alerting mechanisms, and then AI can be put to use for this particular case you're
talking about, you know, can you understand the, the the context or the information
that bad actors may be posting on different kinds of, you know, posts and such. And,
yes, it can be done, and I'm sure, you know, companies are already actively doing
that, yeah, to prevent such things from happening. But in general, you know, this
proliferation of fake news and deep fakes, and those types of things are going to
happen more and more, because, you know, that's the other side of putting technology
to use. And we just have to continue to mitigate it and prevent it with all kinds
of all techniques on all things that we can do with including technology itself, AI
power technology, but also with the right kind of regulations and ethics boards, and
pretty much everything that we can bring to bear to, to, to solve the problem. We
as as society, and we as companies that are building products have to have to do that.
And in some cases, we've noticed that you know, the technology goes ahead us so fast
that does that regulations and policies are catching up late and and therefore, companies
who are driving the innovations have to take the extra responsibility on their own
shoulders to do the right thing.
Cindy Moehring 29:42
Yeah, right. Right.
Rama Akkiraju 29:44
You just can't keep catching up.
Cindy Moehring 29:46
Yeah, yeah. So let's flip that around. There's also another, at least it was a use
case of a very, The Positivity Pump, something Chevrolet had had at least sort of
tested in certain areas of the world, which was a, which was a great use for, like
AI and technology and social media to be used positively. And as I understand it,
it was like, you'd get free gas, if you're the AI analyzed your social media and a
way to see that it was really positive. And with our society, we're kind of where
it is today with a lot of people, you know, in the in their corners, and oftentimes
not being the most productive as they could be, let's say on social media, that seems
like a really great idea to me. Is that, is that something that is was just tested?
Or is that still, is that actually being deployed today? Or what can you tell us about
that?
Rama Akkiraju 30:43
I love that word, when I saw that, the way in which our work was put to use to drive
positive engagements to add the pump by Chevrolet back then, to be honest, I haven't
followed up to see if that is something that was just just done as a proof of concept,
or if they have actually deployed it in places, it might have been a proof of concept,
kind of an idea to write or assess the possibilities of what you can do. But yeah,
I mean, I think that what that highlights is the possibility, the possibility that
you can engage with customers at at different point, various points, including at
a gas pump, based on the context based on the in their interests, based on their personality
traits, and all of those things take into account, right, they found a great way of
offering very engaging services or product promotions, if you will.
Cindy Moehring 31:52
Right. So if your positivity score, you know, on social media was ranked at, you know,
80, you would get more free gas than if it were ranked at say, you know, 50.
Rama Akkiraju 32:03
It was not only free gas, but in some cases they were offering, you know, somebody's
photography lesson. Oh, that's right, an afternoon with the photographer, or, you
know, a session with an afternoon with a chef, if you're interested in, you know,
cooking, you know, with the top chef in your area and a restaurant. So these are all,
you know, interesting ways in which product promotions can be offered to customers.
In a way that is very personalized, right, based on their interests.
Cindy Moehring 32:37
So Rama, let me ask you one other question, you're a very accomplished woman in this
space. And you've had a very distinguished career, I think the perception still is
intact, that women can at times have a hard time succeeding in a in a STEM career.
What advice do you have for young women or minorities that are listening to you and
wondering, you know, what can they do to have a successful career and in a STEM field
like you have?
Rama Akkiraju 33:07
Well, first of all, in STEM field, there are many, many different opportunities, you
know, that ranging from business analysts, to data scientists, to actual programmers
to product managers, to, you know, sales folks and in there. So first of all, one
thing to keep in mind is that the plethora of roles for people with all kinds of backgrounds,
and I would say, increasingly, the need of the hour for companies are these T shaped
individuals, that is, individuals who are who have the depth in a particular subject
matter, whatever that may be. But also, the T, the top part of the horizontal line
is about having the understanding of multiple disciplines. This multidisciplinary
background is so critical for companies to succeed, and you need to be able to understand
the domain of the business and understand whose problem you're solving. Understand
how people interact with systems, the design aspects, and the technology aspects,
and the economics around it. So first of all, STEM is not just don't people who say,
Oh, I'm not so good at math, and I might not be fit for, you know, computer science
or this kind of roles. That's a false perception to have, there are roles for all
kinds of things, especially, you know, the whole conversation that we had today in
this podcast is about you know, a lot to do with compassion and how chatbots will,
or even conversational systems and computer systems and imagine there is so much psychology
behind it, right? It's really at the intersection of psychology, linguistics, machine
learning and you know, retail business domain knowledge. So, I think it's, you know,
the, the yes, there are roles and there are places that are specific career paths
that are highly mathematics based, and that are that are highly based on programming,
but there are so many that are at the intersection of all of these things that anybody
with any combination of these backgrounds can bring, add a lot of value to building
great products, today in companies, so my guidance and advice would be, you know,
there are no limits here, no matter what background you are, you can still come into
a STEM field and and into IT industry, and still make a significant difference. So
that's number two. And I would say, unless you are somebody who's pursuing advancement
of, you know, deep technology, or algorithms or hardware and such, many of our jobs
really require on a day to day basis, if I look at what I do, it's really a lot of
interaction with the design team, interactions with the product management team, interactions
with the engineers, interactions with architects, interactions with management, it's
so there is a lot of hard work and a lot of things that have to come together in a
day, you're not necessarily just sitting in your cabin and and, you know, writing
code or you know, write mathematics. So, for those, especially who have that fear
that you know, I am not so deep in it, majority of the jobs out there don't require
it. So go pursue it. And you know, one other incentive is this is where majority of
the jobs are going. Right, the whole industry is getting transformed. And there is
so many opportunities and, and high paying jobs and opportunities in this field. So
go out there get yourself in, there's there's so many courses that are offered online
these days on on Social MOOC platforms, like Coursera, Udemy, and such. So get yourself
certificates and have to have no degrees and PhDs in this to get a high paying job.
So there are so many opportunities. And this is where the industry is going. Why would
you not want to be in this field and help shape what happens in the next generation
in the whole industry?
Cindy Moehring 37:22
So I love that. So don't think narrowly think broadly. And, and proceed courageously
and boldly. And go for it is what I hear you saying that's great. This has been a
fabulous conversation, I've enjoyed it very much. And I always like to leave the audience
with some recommendations at the end, if they want to go a little deeper. On this
topic, I'd like to ask my guests where you go for inspiration and where you would
direct the audience to go if they wanted to either read something or watch something
or listen to a podcast series to learn a little more about this area. Do you have
any recommendations for us?
Rama Akkiraju 38:05
Oh, there are so many out there. You know, these days, so many folks online are doing
podcasts on these different topics. You know, my my source is really YouTube, I just
go there look for any particular topic. And there are so many professors who are offering
their courses in snippets that you can actually consume, you know, 10-15 minutes snippets
on any given topic. You know, I catch up on several of those kinds of things myself
regularly. And of course, Coursera is a great place to go on any given topic, there
are so many courses. If you're, you know, just a little bit more committed, it might
take more commitment in terms of the number of hours, you know, reading in the podcast,
there are ton of them. Data science related podcasts and cloud related podcasts, AI,
ML, ethics and such, and automation related ones, there are so many I think you only
have to look online to find them. For me, the way I do it is you know, it's a combination
of all of these things and also looking at where, you know, archive is a great place
online for various kinds of academic papers that people are putting out for everybody
to to check out. Go back and look at what's the latest on a particular topic and and
read the abstracts and conclusions of a paper. If that looks interesting, go read
the rest of the paper. And in sometimes I'm on program committees where I review papers
that are peer as part of the peer review process for conferences and that also keeps
me up to date. So there are different venues but these days, information is that is
at our fingertips. It's so much it's just readily available out there for anybody
Those who have curiosity, you can go learn about pretty much any topic under the sun.
Cindy Moehring 40:05
You really can. It's amazing so well those are great recommendations and great like
just directional places for people to go and and things for them to check out. So
Rama, thank you. This has been a fascinating conversation. You've been very generous
with your time and, and your knowledge and your wisdom and your thoughts and explanations.
And I know the audience has taken away a lot from this. So thank you very much for
sharing with us.
Rama Akkiraju 40:30
Cindy. Thank you for having me on your podcast. I've enjoyed our conversation. I hope
your viewers will get something out of this, whatever 20-30 minutes that they spend
on it. It's been a pleasure getting to know you and your accomplished career and you're
doing a wonderful service to your students and the community by bringing different
folks to your podcast. So thank you for your work as well. And it's been my pleasure.
Cindy Moehring 40:50
Well, you're welcome. This has been great. We'll talk again soon. Thanks Rama. Bye.
Bye. Thanks for listening to today's episode of theBIS the Business Integrity School.
You can find us on YouTube, Google, SoundCloud, iTunes or wherever you find your podcasts.
Be sure to subscribe and rate us and you can find us by searching theBIS. That's one
word, t h e b i s, which stands for the Business Integrity School. Tune in next time
for more practical tips from a pro.
Cindy Moehring is the founder and executive chair of the Business Integrity Leadership Initiative
at the Sam M. Walton College of Business at the University of Arkansas. She recently
retired from Walmart after 20 years, where she served as senior vice president, Global
Chief Ethics Officer, and senior vice president, U.S. Chief Ethics and Compliance
Officer.
