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.”
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
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
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.