In this episode, we'll be focusing on artificial intelligence, especially the use of natural language processing and virtual assistance powered by Watson. To explore this topic in depth, we're going to share two conversations we recorded with leaders at IBM. The first is Rittka Gunner, who is Vice president for ibm S Data and AI Expert Labs and Learning, and the second chat will be with Jay Bellissimo, who is IBMS General Manager for the U S Public and Federal market. Rittica, thanks so much for joining us today. So to start off, can you introduce yourself and talk a little bit about your role at IBM. Yeah, thanks, Joe and Robert. This is a pleasure to be here. My name is Ritica Gunner and iron a organization called Data and AI Expert Labs and Learning, and our whole mission is to be able to help clients be really understanding of what it means to adopt data and AI technology GS how we help them accelerate UM the use of data and AI across their organizations through the methodologies and through the skills and expertise that we have working with thousands of clients, so that is they're embarking on their AI journey they know how to be able to do that quickly. Excellent. Now, in this episode, we're obviously going to be chatting quite a bit about AI. So just to ground our listeners in the right place, can you define artificial intelligence for us and describe the sort of a I will be discussing here today. Yeah, so artificial intelligence, many of us know the hype around it, but very simply, artificial intelligence is about teaching machines to interact and think and make decisions like humans will. So it's around us every day when you look at how machines can actually see things like humans do when they can speak. Many of us have those UM consumer type applications like Alexa, Google Home. UM, they're even UM enterprise type versions which I'm gonna talk about today with Watson and Watson Assistant, where you can have assistance either in the home or for commercial use. UM, whether it's actually doing any types of areas where you can predict or optimize or automate kind of working decisions using artificial intelligence. Excellent. Now, you mentioned Watson AI and Watson Assistant. UM, can you ground um those two for us as well to tell us you know what exactly Watson AI is and as much as is feasible at this point in the interview, UM, what Watson Assistant is sure UM, Watson AI is leveraging UM technologies for AI for most enterprises and their essential workloads. When you look at it, we've done thousands of AI projects UM, all across the world, across almost every industy tree you can imagine, and almost every country, and through that we've learned what it means for clients to truly put artificial intelligence and their most essential types of workloads. Is part of that. We've developed Watson AI capabilities. Some of you may know the simple kind of a p I s that are like vision, speech, natural language processing, but it's also more than that, and I'm going to talk about that in terms of the applications that we see many users leveraging AI in accelerating how they're actually doing this. So we're going to talk about things like Watson Assistant, what we have in terms of Watson capabilities for the financial services organizations, or what we do to be able to apply AI to any kind of industry. As we do that in a way where we can actually prepackage that application. We have Watson AI applications, Watson Assistant is one of those. Think about this not just as a simple chatbot, but it really is about letting users have the ability to have an assistant that understands how to respond to questions that you've been trained on or that it has learned through data that it's seen previously on not just really simple questions, but really hard questions that may be sitting in documents that are buried inside your organization, whether that be you know, your playbooks, whether that be UM, your run books, that you have to be able to answer questions. It really is about answering not only the simple questions, but training AI to answer even on the most complex questions as well. So today we're going to be focusing a lot on how AI is being used in the current situation that we're facing in the world. Can can you talk a bit to ground the problem about UM some of the ways that the COVID nineteen pandemic is affecting how the public interacts with businesses and institutions. Yeah, I'll give you a little bit of a background. Three out of four users and they have a problem, any problem, they want to be able to answer that problem on their own. That's why we use things like our phones, our web internets, are our voice assistants that exist there. Three out of four people want to be able to find an answer without talking to someone and to be able to do it on their own. When you look at those users, um, two out of three of them actually consume three or more channels. That's you know, using your web, using your voice assistance, et cetera. So there is a large demand for people to be able to find answers to questions on their own and to be able to do so in multiple mediums. Now, um, when we look at what that means on the back end today, we have multiple customer service agents that are answering a lot of those questions, and over of those those customer service agents don't have answers at hand, and so that becomes quite frustrating for end users who really want answers at their fingertips. So you know this, the situation that we're embarking on is the use of more pre training kind of AI capabilities to be able to answer those questions. This pandemic that we have with COVID nineteen is no different. I want you to think about the amount of uncertainty that's there in the world because of COVID nineteen. You know, a few months ago, at the very beginning, it was what are the symptoms of COVID nineteen? UM, what do how do I tell whether my own child may have COVID nineteen? And what was happening is a lot of the call volumes that were coming into the hospitals the government organizations were overwhelming the system such that doctors and nurses and public servants were spending a lot of time answering questions versus facing the pandemic itself. And so we saw a huge surge in request to be able to use Who's AI powered assistants like Watson Assistant to be able to answer those questions. And it has expanded from just help me understand what the symptoms are, help me understand you know where I can go get tested too? Now other things that are downstream, like you know, what are unemployment benefits for my state? How do I, UM, how do I actually apply for a small business loan? And so the demand in times that are so uncertain, especially when you look at how every hospital, every county, every country has their own um types of of regulations or or or rules, a I become something that's really powerful and that's what we've seen UM through this pandemic. UM we have an offer with Watson Assistant that we've put out there where we are making our technology is available for many of these organizations are dealing with this pandemic, and UM we are. We are trying to put these technologies up usually in less than one day, not just with an assistant, but a voice integrated assistant, and do that to where our our clients can get up and running in less than a day servicing their constituents, deflecting up to the call volumes that usually would come into a call center. But you have to realize that the call centers themselves have their employees working from home and are constrained themselves. Let me go through a few examples that we've seen during this pandemic that I think that will relate to not only you, but to the audience. We have UM hundreds of assistance that are now alive over twenty countries and a lot of them are responding to real time UM questions around COVID nineteen. I'll give you one example, which is my home state or my home city of Austin, Texas. We have a Watson Assistant on their home page for the City of Austin, providing instant answers to their citizens about UM, the COVID nineteen situation in Austin, where people can go get tested, and the most up to date information there. Another example is the Children's Healthcare of Atlanta. If you think about it, parents are really worried about what does it mean for their children? Do their children actually see a lot of these symptoms UM and if they do, where should they go to bring them in? We worked with the Children's Healthcare of Atlanta to be able to put not just any assistant, but a voice activated assistant on their site within a weekend UM to be able to be up and running. As I mentioned, we're now up in live and over twenty countries UM. Some other examples are the check Ministry of Health launched a virtual assistant named Annesca to guide citizens on topics on the coronavirus according to policies that were set by their fro MINT and you know, in the first weekend, what they found is that only ten percent of chats required a handover to a live agent. Just think about that. That means of questions were answered by this assistant. As I mentioned, like the first waves we really saw we're in the public sector and in healthcare, but now we're seeing that pervasive across all of retail, financial services, industrial, We're seeing it across almost every industry because there is a spike in and demand. You know, a lot of these started out as as assistance for citizen communities. How do you let the public know what's happening, But think about some of the other use cases. Some of the other ones are as I mentioned, um, you know, actual companies and organizations that need to service in their customers because their landscape is changing, or even companies that need to service their internal employees. One is it okay to come back to work? What are the regulations on coming back to work? How are you phasing it? And so you need to be able to have these kinds of capabilities to respond to a lot of the uncertainty in these times, and AI definitely helps. So I wonder obviously a big thing when you're having, say, uh, some kind of call routing program, an assistant that would be dealing with calls coming in. One thing is that has to be able to decide when somebody needs to speak to a human operator versus uh, you know, continuing through the call flow, whatever decision flow it naturally has what goes into that kind of decision like and and how important is making that decision early on? Yeah, actually, I think that's actually a great question. Um, think about what it means to train an assistant. You have to be able to first when you ask a question, understand the intent of that question. So when you're asking what are the symptoms of COVID nineteen, you have to be able to parse that sentence in natural language and really understand what is that intent, because that could mean different things. If I said, um, you know, instead of what are the symptoms? If I said, um, where can I get tested for COVID n team, those are two separate intents, and so we call those things intent. We have to train Watson assistant on the types of intent to be able to answer, and the answers to those can change over time. So we talked about some regulations change naturally as these organizations actually change, and so the first thing is having an understanding do I understand the intent? And with what probability do I understand the intent? Majority of the time these intents are easily understood, and so if it's an intent that has been recognized, we can answer that question if Watson has been trained on part of what are those answers? And so we sometimes see that if you can recognize the intent, then underneath there you want to be able to answer those questions. If Watson has a probability then range of what the user accepts as reasonable, it will automatically respond, and if not, you can train Watson to then kick it back immediately to a actual, um physical person to be able to answer those questions. Oh, that's interesting. So the uncertainty can be the queue that we need a human to intervene here. If you think about it, AI is all about probabilities, right, What is the probability that the answer that you have is most likely to answer that the user is looking for. If that probability is high enough and in the tolerance range that you have, then you can actually give that as an answer. Yeah, that makes sense. So to to unpack the technology a little bit more, UM, I think from the caller's point of view, I think we've all had these experiences where we know that in some cases automated menus can be very frustrating, right, Like, uh, I'm imagining calling customer service at a credit card company or an Internet service provider. Obviously, like we realize we're talking to a machine been and without a human operator, we might worry that some important information we have is being ignored, or some nuance of our case that that we're trying to deal with doesn't fit within the automated routing stream. UM, So, what are the ways that properly designed AI assistants can help make automated call routing both more practically helpful to callers but also more emotionally reassuring. There are many things that are happening in this space that I think are really exciting. Look most of the time when you call into a company for customer service, when you get kind of the automated machines that are like press Wan press too, those are very deterministic UM systems. Those are not using artificial intelligence. If you look at the real value of having assistant like Watson Assistant, it's about being able to answer your questions when you want them as you want them, not in a pre integrated workflow that you have with other deterministic systems. And that's has a completely different experience because you can start in one part of a conversation you can go multiple levels deep. You can then go start another thread. And because the way the AI components as well as the logic works, you are speaking naturally, you are getting answers in a natural way, and you are actually going between threads multiple levels deep. That is engaging as you would as a human And what you can do with wads and assistant and what makes it such a beautiful experience comparative to what customer service experiences like when you have those deterministic voice capabilities. Now, one of the um aspects of calling and engaging with the human operator on the side of the line is that this generally there is an expectation that they might be empathetic and give appropriate responses for say, delicate situations like unemployment claims or health fears. Uh. You know, And again that may be the expectation, if not the actual reality. But then how do you tackle that from an AI standpoint? How do you make sure that it is at least seeming uh to speak with with empathy and give those appropriate responses to someone who is in need. You know, It's part of understanding the intent that we have not only with Watson, but other parts of our portfolio. We can understand the tone and the sentiment of a user, and so we can understand if that tone is positive. We can understand if the tone is that the end user is irritated or if they're extremely angry. A lot of that can be understood not only by the words that the user may be typing UM into the screen, but by the way their tone is when they even speak. And because of that, we can actually respond in similar ways where we have the right level of empathy to respond back with. In some cases where we find that, you know, suppose a user is extremely angry, we can pass them off immediately to a human and put that human in the loop. And that's why, you know, when you think about artificial intelligence, I always think about it as an ingredient in the broader picture, and it actually is something that helps your overall application or your overall customer service experience and is human assisted. Like the human needs to be part of that loop at some point if if there is an escalation UM. So, understanding tone and sentiment is an extremely important thing, especially when you're dealing with customer service. UM. You know, we see that quite often. You know, you want to understand, for example, if you have been bill double and you are extremely angry about it. The assistant itself can take care of that with empathy, but perhaps it's something that you want to pass to a a a human agent to be able to have a little bit more handholding in that particular situation. What we've actually found is, you know when huge enterprises, when when large enterprises get started with AI capabilities, they'll actually start in a way where a I can also be used for their assistance. If you think about it, you want every every assistant in your organization, every human assistant, to be able to give the same answer in the same way, so that there's no discrepancies on what the right answers are. So we see assistance not only being used directly for the customer, but also for agents within organizations to have the same response to all types of questions coming in. So when we when we think about bias, we tend to think about human activities and human institutions, but of course AI as a human creation is susceptible to bias as well. Can you explain how bias creeps in UH? And then how do we prevent AI systems from succumbing to these same errors? If you think about it, bias exists every where in the world in the natural world today. You know, if you take a look at AI systems, AI systems are naturally trained by data data that has existed there in the world for decades centuries. I'll give an example today. If I would train an AI system on how to approve alone for a particular person, and I use data from the past fifty years, more than likely you will see bias in the data for the last fifty years. Were given everything equal that men were more apt to be able to get alan with all the same attributes than a female would. That is biased that exists in data that real people have approved loans over the past five decades in that particular case. You know, if we train AI on that same data today, we would want to make sure we take that data and we remove that bias, because that is a fact that we would want to say, Okay, there's bias that exists in the data that we have. We can see the bias, and that's a bias that I don't want to be able to have. Another example maybe about claims and claims approvals for auto insurance based on age. We know, as an example, if I take the last fifty years of data that claims that come in from younger generations are probably um more prone to some sort of fraud than older generations, and that particular case, you might have a higher fraud rate at you know, the at ages eighteen to twenty four as an example. That may be a bias that you want to be able to keep in your system of approving or not approving claims, because that is a reasonable bias to be able to have to say, I want to double check before we approve or not approve those type of claims. So bias exists naturally in the data that we have. And given that AI is only as good as the data that you train it on, you need to be able to take the algorithms that you create from AI, you need to be able to detect where that bias exists. And then in cases like I mentioned where you're talking about age and loans, you want to be able to remove that bias. And that is one of the most critical factors to making AI mainstream. Like I'm a firm believer that this is a decade for AI to go mainstream, and for it to be able to go mainstream means that organizations need to be able to have trust in AI and how that AI is working. And that's why being able to take any model and understand where the biases in that particular model, and then to be able to understand things like, um, you know where, how can I explain how that particular model made that decision? What are the factors that went in too? For AI to make a decision becomes extremely critical. Um So the ability for organizations to make that AI mainstream is can I detect bias? Can I remove it? Can I explain what AI is doing? And that's a lot of what our teams within IBM are working really hard at. A lot of the research technologies that we've had are now in our products, and we're helping many of our many organizations large and small, be able to take the AI capabilities they have and to put explainability and biased detection, UM and fairness recommendations into their AI components because that's the only way we're going to get AI to scale. Yeah, I guess, I guess what I was wondering there is if this is a case where like a diversity within the tech world actually has measurable effects on whether these types of bias uh end up making it through, uh, you know, or go unnoticed in the design stage. I think that's a good point. Look. Diversity in technology and especially in artificial intelligence is critical UM, and that's diversity in all kinds of backgrounds. I would say from UM, having diverse perspectives and diverse point of views help you create a that is more beneficial for society and for the community. Let me give you a couple of interesting statistics UM. You know, we we have recently put a lot of focus on women and artificial intelligence today. It's estimated that less than twenty of A professionals in the marketplace today are female. That's not where we want to be able to see UM the representation of A on females because as you have more diverse perspectives and points of view, you can create better outcomes for users and better AI algorithms. And so it's one of the reasons why IBM has put such a huge focus on women and AI. This year is the second year that we announced a Woman in AI program where we have UM celebrated over thirty females in AI professions and the journey that they have taken through their career. We have a few goals in and being able to do that. UM, let me let me first take a step back and tell you, like our our effort in doing this was to really promote gender equality and AI and showcasing these over thirty leaders across a variety of industries I think they're in like twelve countries was really important to us to demonstrate not only the power of AI, but the power of diversity in AI and the kinds of accomplishments that these organizations are doing in the technologies that they're implementing with Watson and other capabilities. So you know, what we what we have found is that you know, by having and highlighting a lot of these females, we can interest other younger generation of women to be able to embark in this AI career. This is especially touching for me and I think UM a lot more active in the women in AI field being a computer scientists working on data science and data science products for a while as I have young children and my my daughter who is nine years old, came to me UM one day after I sent her to a Python programming class and said I don't like it. And when I sat down with her and really understood the threat of why don't you like the class that you're in. You know, it's a programming class where we're engineers at heart, and you know it's one of the things that you have to go do. She said, I was the only girl. Everyone was coding Minecraft mobs, and I really wasn't interested in that. I was sitting by myself, and it was in that moment that I thought about, you know, people need to be able to see themselves in role models, and that's why I think programs like Women in AI are so important, because as we want more diversity, people need to be able to see themselves very clearly in that. I'm pretty proud of some of the things that IBM has done to be able to put more women in AI, not only within our organization, but to be able to promote that with the thousands of clients that we work through in this second inaugural program that we have. Now, when the COVID nineteen pandemic ultimately subsides, what lasting impact do you hope to have with the work that you're doing right now? You know, I'm so proud of our teams that have risen to the occasion of helping these hundreds of organizations implement AI capabilities during this time of crisis. If you look at it, it is helping answer some of the most pervasive questions across all of these organizations in an extremely timely manner. I think the change is that a lot of these organizations are embarking on are here to stay, and not only here to stay, but they're going to accelerate for every organization. Every organization is going to adopt technologies like AI digital capabilities a lot more quickly, and so a lot of the lasting effects are taking what we learned and helping organizations really scale out a lot more quickly the use of these AI technologies and having them be fundamental to how they operate and not just a side car. Again, much appreciation to Ridikagoner for taking the time to speak with us for this episode. And now we're going to go straight into our second talk on the subject with Jay Bellissimo. All right, well, Jay, we really appreciate you taking time to talk to us today. Can you start off by introducing yourself and telling us a little bit about your background. Sure, Joe and Rob, it's great to have the opportunity to spend some time with you today. So my current role at IBM as general manager our Federal and Public business UM. Prior to this role, I spent six years focused on UM Watson and AI and cloud excellent. So on the on this topic of AI UH, as we you know, dive further in in this interview and start chatting about these about the Watson, AI, Watson Assistant, etcetera. Before we do that, would you mind just like touching on some of the biggest UM misconceptions about AI itself so that our listeners are properly grounded and where we are with real world AI. Yeah, Rob, it's a great question. Again, I started as a general manager in our Watson business back in two thousand fourteen when it first started. And you know, there's always been a lot of talk about AI. AI algorithms have been around for many, many years UM, and I'm pretty excited because since two thousand fourteen, I've I think I've averaged about two hundred thousand miles a year, been a thirty six countries, been evangelizing around AI UH and I couldn't be more excited. Unlike four or five years ago when people said, well, when's it gonna happen, It's it's happening, right, It's not a question whether or not it's gonna happen. It's here today, whether it be every day you know, listening Spotify or using ways. I mean, it's in our everyday lives. And we've also seen this whole consumer market really blur the enterprise, you know, companies and government organizations. So where we are this year, I playfully say this is a year AI goes into production. Just at IBM alone, we have over twenty thousand UH projects locally UH in terms of you know, AI use cases, and I think at the end, you know, Rob and Joe, For me, the difference is what's the problem you're trying to solve? Right, Sometimes we get out ahead and say, you know, companies will say well, I must have a I. But but practically it really comes back to AI is awesome and it can do so much, but you really have to have in mind what's the business problem you're trying to solve. And once you can really hone in on that, then it becomes a lot easier to look at it because in addition to AI, another big big piece of this is the data. Right, And you've heard so much about the data and access of the data, and that's one of the things. We take a lot of pride with that ID end is you know, typically that data is the hospital's data, or that data is a government agency's data or its or it's a big industrial companies data. Because that is that is really important because ultimately that's your I P. Because when you think about it very simply, when you think about AI, it's you're starting with the data and working with AI. The data is going to create insights. An insight creates knowledge. And with all that rob coming back to your question, that's where I get really excited because in the end, this is really a partnership between man and machine. It's not man verse machine. And and that's important because let's face it, there's so many menial tasks that can be automated and that's no different if you look back over the industrial revolutions over the last hundred years and so really you can use this technology to do so much, but it's there's this misnomer that is displacing jobs and that couldn't be further from the truth when you really think through how this can be applied. Is it automating, Absolutely, but there's so many jobs that are being created that we need to make sure that we not just our company, but any of the company's academia governments. We need to come together as stewards of this next generation of opportunities and all of these new jobs and make sure we ushered in together responsibly. Because I'm not worried about the jobs. What I'm worried about back to all the clients have been at hundreds and hundreds of clients over the last six years. Um, it's how do I equip my workforce to to stay current. And that's the part people really need to double click on and make sure that we're all responsibly making sure that everyone has can transition with into this new era with the required skills. And the last point is just on as we do this, there are still some challenges around ethical, ethics and bias, and that's something again everyone, academia companies like you know, we all need to continue to work together to make sure again we ushered this in and if very responsible way. Maybe we should focus for a second on specifically what some of those challenges are. You mentioned of course, ethics and bias. We can maybe circle back to that if if we want. But another thing that I saw you mentioned I watched part of a talk that you gave where you mentioned the idea of information architecture that you know that you can't have AI without I A. Could you talk a bit about that and what kind of challenges are still present there? Sure? Sure, But fundamentally, what we're saying with that statement is basically, the data is going to be critical and when you think about the data and what you do with the data and you analyze, right, you collect the data, then you analyze it, you organize it, and then you infuse it with a I. That's really what we're saying is when you have a I, you really also need to have that information architecture because they go hand in hand. Because again, we've had the data, but we've never had the data at the levels we have. It's it's really outstripped human of the ability to keep pace with it. So with that we have all this on structured data of the world's data is unstructured. On top of that, only of the data is access via the web, so a lot of that data sits behind the corporate firewalls, right, and so there's so much opportunity. So in the end, you've you've traditionally had information architecture. So even though it's this new way of AI. They really go hand in hand. So obviously today we were gonna end up focusing a good bit on how AI is being used to respond in the wake of the COVID nineteen pandemic. But first just to establish, you know, what what the needs are, what the problem is. To begin with, can you talk about some of the ways that the current pandemic has affected the how people interact with businesses and institutions it as you well know, I mean, none of us expected what we're living in right now. And the really cool thing is the way everyone's coming together across the untes. Recently, I had a call with the general manager of IBM Italy and IBM Spain and we were just comparing stories IBM China as an example, and we were just exchanging over the last sixty days what we've all seen and it's pretty powerful, the way everyone in the face of all this adversity has really come together. And what was interesting a thread that we saw across all these different companies was we start with empathy, right and as much as you know we we we talked about business, this this goes far beyond business, and this is really being empathetic. Every country is going through a different, different phase of this pandemic, but this common threat is just really listening, talking to clients, talking to government organizations h HS, Health and Human Services and others, and just really listening what what are those problems that we're trying to solve together across corporate boundaries. And so a great example I would use I was personally involved with the Learn's Healthcare of Atlanta, and I remember it was a Sunday, and I had spoken to the c i O and their issue, which is I think pretty um common across the law of the hospital and healthcare providers is in their case, their nurse station was being overwhelmed understandingly by a concerned citizens. Maybe it's a parent calling about they're worried about maybe their child has a fever, or there could be other symptoms. So they were just overwhelming the nurse station. So within forty eight hours from that first conversation with the c i O, we were able to stand up Watson Citizen Engagement UM bought a virtual agent we like to call them and and basically helped them so when they those calls came in, this virtual agent could take a lot of the questions and in a very natural way, very interactive way, engage with citizens who were concerned. And the way we trained it is the hospital heads to civic protocols, and protocol would be as an example, my my child has a fever, what do I do right? So the protocol is just an encapsulation of all the different variations. So in the end it really is consistent with the hospital's procedures. Every hospital could be different with the protocols they run. The exciting thing is the problem was that the nurses needed to spend more time with patients as much as they wanted to spend times consulting and using this technology to be able to interface with and being being with the citizens as they called. And then at the right time if a nurse or another medical professional need to be engaged, then this citizen engagement. But can then help what we call our Watson assistant for citizens. Um, what this can do now is now we directed to a person as needed. So the beauty is it helps front end it and answer those questions. But if they get to a point where they're not sure, then enhance it off to a person and that would probably be again a great example. And then another really good one that has come up with a lot of states that we're we're working with, like the state of Pennsylvania is unemployment insurance as you Joe and Rob no. I mean, some of these systems very public, uh in some of the states like New Jersey, you know, the call for COBAL programmers or others where they're just overwhelmed. I think there's as many as thirty million unemployed over the last six months. I think the unemployment rate is around four to five percent right now. And and so again, using some of the same Watson Assistant for um citizens technology, we've been able to help states like Pennsylvania implement these types of solutions. So maybe could we imagine UM walking through what one of these experiences might be like from the caller's point of view, because we all, you know, have experience with probably not a I powered but more deterministic traditional call routing systems, like when you call your edit card company or your internet provider or something, and it it can we all know, be a kind of frustrating experience. How does an AI powered experience differ from that? How in what ways could it actually be more practically helpful and potentially more emotionally reassuring. Yeah, it's a very good question and and and ultimately it's interesting and this is all about you know, serving the people in the communities. Um. Having said that, you know, some of the technology solutions are different. Some are programmable, you know, chatbots, and they're really light in terms of real intelligence of what they do, and in those cases they can frustrate people calling into these when you really factor in machine learning, as we do with our Watson technologies and solutions, again, it comes back to the four areas I talked about before. It's understanding, right, and there are nuances in the language and how we communicate with each other, so it understands the context of the words and the phrases. And to your point, Joe, Um, these systems are probabilistic, not deterministic, and that's a game changer in my view. And that's why we're pretty excited about our Watson in our AI technologies because it really to your point, it's it's more empathetic. It engages genuinely with people that understands the words they use in the language and they know how to respond. And then to that third point, they continuously learn and they get smarter and smarter with every interaction. So they're not perfect, but every time you learn from past experiences, then it's only going to get better and smarter, and it will be more engaging with with citizens or consumers depending on how you use the technology. This is a fascinating thing to think about because UM essentially we're talking about having a more human experience with the technology. Uh I. Like you said, most of us probably have experience with UM with the other model of of automated UM, you know, machine tech chnology. When we call a credit card company or whatever the case may be, we feel ourselves just thrown into those brackets, and it feels it can feel dehumanizing, it can feel very frustrating. UM. I wonder as as we as a population begin to experience more and more of these AI models, UM I imagine people are are going to go into them, perhaps expecting that frustrating, UM sometimes dead ended situation, but instead they're going to encounter something that is reacting to them more that may even in these cases be exhibiting something like empathy. UM. How do you see that that shift going with us as a technology using culture? And then is there a is there a potential that we overshoot then and we start expecting more empathy than is possible from the machine. It's a great question, Robin, and in the way I look at it as anything you can do to meaningfully engage with citizens and consumers is a very good thing. We have different technologies with our Watson technology, we have personal personality insights and other types of capabilities that it's trying to enrich that interaction. Um But at the end of the day, you know, every company will have their approach and our approach has always been again man and machine, and we're always trying to make sure we have the most meaningful engagement between man and machine. And to your exact point, uh, the empathy, you know, how are people feeling that the way they use certain words? What does that mean? Can we drive more insight from that data? And ultimately for us, the game changers knowledge from the insight and that's when you really get into this I think a much higher level of interaction. But you also govern that in terms of how far do you want to to go down that path. So in example, just to extend this conversation so you have that engagement, right, it's understanding, reasoning, learning, interacting with people, in natural language ways. But now when you look at that, um, then you get into the point of okay, so you've got this trust and you have this new way of engagement. You can you can interact by the use of words because you're getting smarter. And again it's systems like Watson never forget, and that's the power to get smarter and smarter. But then you extend it more and you say, okay, maybe, UM, we could take an example. Let's say Rob, you and I are applying for a mortgage at a bank, and and maybe you and I interacting with one of these systems. And this is a fictitious example, but there are examples like this. But maybe you and I are interacting with a with a system in AI system, not our system, just any system. Right now, what if you and I check all the boxes but in the end you get approved, I get rejected. So you could say, well, wait a minute, this doesn't feel right. I know he's my friend and we have similar capability, so why so you get into this whole explainability of a I know what happened in those neural networks. You get into bias. How do I know Rob? It wasn't biased against me? For some odd reason, and so you need to have explainability. And so again we have some great capabilities in IBM and our Watson solutions where we we trace that we can flag where there's potential issues with bias or explainability, because we want you to have that full traceability across end to end. And that's the other kind of evolution. So back to your your original question, Rob, we absolutely want to enrich that experience. We want to learn from the words you use and and tighten that communication and loyalty and trust. But with that trust becomes a big responsibility to make sure a you're protecting the data. You've got to be secure to the core in these systems, and then to extend it back to explainability, back to removing bias. A lot of that last part on biases. How are you training the systems if you're putting in uh, let's say nurses and doctors from you know, data dumped from last thirty years um today you know there's a great inequality with women and men, and and we're all doing our part to make sure we conn accelerate the turn or the pivot we need to make. But in that case, what if you pump into a system thirty years where it's been skewed where nurses are typically women, right, and now you're feeding out data into some models um and and these algorithms, and at the end of the day it might give a different result, but you might scratch your head and said, well, over the last five years there's a lot more male nurses than there were thirty years ago. So that's a point of again a responsibility. It's so important to have that end to end process on how again, what's the problem you're trying to solve. And then as you go through that end to end you've got the data responsibly. You have to load the data, you have to understand the data, you have to protect the data again that's IP intellectual property. And then as you feed into the models, you want to have that explainability, traceability, and ultimately the ethics which equates in large part to the bias and and so you have that responsibility and that at the end of the day, that's what IDEM is very proud of. We have tools and capabilities to make sure that there's integrity throughout the whole end to end process. So obviously the COVID nineteen pandemic has has forced UH companies and institutions to to take up AI technology to implement it. Um, you know, just to to get through this time period. But what lasting changes do you do you see, um really sticking with us from this that are really going to benefit us in the long run. Yeah, it's a great question, Rob, and I think, you know, I don't think anyone has the exact answer, right, And we are in a new norm and it's to be defined as we move forward. But when I look at a lot of the AI and other technologies, blockchain included, and I look at organizations that maybe are you know, in a function. It could be a government organization and let's say they're they're um, they're remit is to make sure that critical supplies move across their supply chains. Right. It could be let's just say, ventilators, instead of redirecting a hundred ventilators to Atlanta, maybe you redirect them to New York City. Right. Um. So we know there's a lot of great technology out there that does that, but a lot of that beyond the predictive analytics, it's gonna be situational awareness, that whole operational situational awareness. And so that's just an example of supply chain. So right now it's pertinent because it's COVID nineteen. But what if you put that solution in there, and we are exploring some things with one of the organizations UM, and you have a short term solution, but over the longer term, what if it's hurricanes And this next chapter you're gonna be using a lot of these same technologies and potential solutions, and you'll just iterate on these solutions. So I don't view a lot of these solutions. Going back to the Children's Hospital of Atlanta. Yes, the immediate need is to to be that UM Watson assistant for citizens, but over time that could evolve to other areas where they want to use AI. Back to my earlier point about scaling in other use cases across their hospital, or back to this organization. And then the only other point I would highlight is UM Again, there's a lot of great technology out there today around data and analytics, and our view AI is very complementary. But let's go back to hybrid cloud, right, and let's go back to Kubernetes and red hat and and how all these things are coming together. These are game changing, transformational solutions and and so beyond the covid um period here at the pandemic um, there's gonna be a lot more opportunity to continue to take those and go faster and further. And and so my point being is when you look at coming back to the logistics, I'm gonna shift gears just for a quick moment. Look at what we're doing around food safety with blockchain, right, We've been partnered with Walmart, um and others around making sure that you've got better traceability across that whole supply chain because people get sick and some are definitely sick um over some of these uh scenarios. So if you can take blockchain, in AI, in cloud and move put all these together to provide these solutions, this is there for the long term, right. And so again we come back to point solutions right now in this immediate crisis. But I think a lot of these solutions live on and I think they get better. I think they scale. And back to the point about maybe now it's worried about ventilators, Tomorrow it could be worried about a hurricane disaster and moving other critical supplies across the US or even globally. You know, we we cover a lot of inventions on this show. We discuss how different technologies emerge and how they're rolled out, And it really is fascinating to think about about AI UM and and these examples you've you've brought up already, uh and and just how different it is from other technologies. So many of these technol have come out and and it's it's you know, it's instantly going to be used for our uh you know, our our baser instincts, so or it is going to be misused in some fashion because there's not you know, how how ethical, how how how how ethical can can an invention be for the most part when you're talking about some sort of you know, basic energy um UM technology, But with AI you're you're talking about the the the ethical use of the thing being rolled up, in its development, in its actual existence. That's exactly right. And again, as I said at the outstart, I mean we all have a responsibility, right IBM competitors, governments, UM institutions. In that earlier example, Rob, when we were we were going through that fictitious example of you and I applying for for loan, what if I got kicked out? You got to prove just think internal to that bank, they have to go through their own validation and due diligence to make sure it's clear. And then you might have on top of that regulators that we want to look at this. So it does bear a lot of responsibility, but the point is we all have to usher this in in a very responsible way. And UM I have the good fortune of sitting in on our part of our AI Ethics board with an IBM, and I can tell you we we have great focus. We meet literally every week and we're always pushing ourselves to think about these types of things, whether it be UM you know, UH facial detection or just the way we talked about some of the uses of this technology. We we have to be very responsible and make sure that again we're working together and ultimately with the governments. Every country is different, right in Europe. You've seen a lot over the last year, especially with g d r P and other types of requirements, you know, whether it be the cloud, the the AI data protection UM so every country is a little different. I learned a lot of that just being in countries and Singapore and Japan, in Thailand, and everyone does it differently. But regardless, the great thing about IBM. From my perspective is that we're in all those countries and we're very active with the governments, and we're very active with the companies, were very active with academia like M I T S, and we're all working together to find out and explore what what are those ways because as you know Rob and Joe, it's an evolution. There is no set answer. It's always changing and this technology is only going to go faster and be better, and that needs to We need to ensure everyone's on their toes, so to speak, because it's a big responsibility. I'm I excited about AI, super excited. It's a game changer, but only if we all make sure that we're developing it in a very responsible way and we're working together to make it happen. All right, So there you have it. Thanks once more to Ridka Gunner and Jay Bellissimo for taking time out of their day to chat with us here. And if you'd like to learn more about Watson Assistant, just go to IBM dot com slash Watson, slash COVID dash Response and you can also check out IBM dot com slash smart Talks for more information about the topics were discussing here and if you would like to listen to additional episodes of Stuff to Blow Your Mind, you can find us wherever you get your podcast. We just asked the you rate, review and subscribe huge thanks as always to our heroic audio producer Seth Nicholas Johnson. If you would like to get in touch with us with feedback on this episode or any other, to suggest a topic for the future, or just to say hello, you can email us at contact at stuff to Blow your Mind dot com. Stuff to Blow Your Mind is production of I Heart Radio. For more podcasts for my heart Radio, visit the iHeart Radio app, Apple Podcasts, or wherever you listening to your favorite shows. 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