550: Building Accountable Artificial Intelligence | Paul Zikopoulos – Part 1

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Everyone’s biggest buzzword right now is Artificial Intelligence, but what is it really, and what does the future hold for AI?

Let’s find out in our 2-part episode as George Leith chats with expert Paul Zikopoulos, the Vice President of IBM Skills Vitality and Enablement to discuss fundamentals, ethics, and the future of AI.

Paul is an award-winning professional writer & speaker who’s been consulted on AI & Big Data by the popular TV show “60 Minutes”. Paul’s been named to dozens of global Experts to Follow & Influencers lists, including Analytics Insight’s Top 100 Global AI & Big Data Influencers. Paul’s written 21 books – including The AI LadderCloud without Compromise, and 3 ‘for Dummies’ titles – and over 360 articles during his accidental 28-year career as a data nerd. 

At IBM, Paul leads from the front, helping to shape the strategic direction in a ‘tech years are like dog years’ world for the IBM Technology Unit’s (all IBM software & hardware) sales, tech sales, and partner ecosystem learning journeys and upskilling programs.

Paul actively supports Women in Technology and is a seated board member for Women 2.0 – now called Switch which he became involved with after his tweet was mentioned on the TV show, The View. He’s the only and first male to win IBM Canada’s Women in Technology Ally of the Year award, and is at the forefront of general workplace inclusivity, completing an intensive D&I certificate at Cornell University. Lastly, Paul is a seated board member of Coding for Veterans and sits on the world-recognized Masters of Management Analytics & AI program boards at Canada’s prestigious Queen’s University.

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Building Accountable Artificial Intelligence


George: This is the Conquer Local podcast. A show about billion-dollar sales leaders, marketers leading local economic growth, and entrepreneurs that have created their dream organizations. They wanna share their secrets, giving you the distilled version of their extraordinary feats. Our hope is with the tangible takeaways from each episode, you can rewire, rework, and reimagine your business. I’m George Leith, and on this episode, we welcome Paul Zikopoulos. Paul is the vice president of IBM Technology Group, responsible for skills and enablement. He’s an award-winning professional writer and speaker who’s been consulted on the topic of AI and big data by the popular TV show “60 Minutes”. Paul’s been named to dozens of global experts to follow and influencer lists, including Analytic Insights, the top 100 global AI and big data influencers. He’s written 21 books including “The AI Ladder”, “Cloud Without Compromise”, and three For Dummies titles all around AI. And over 360 articles during his accidental 28-year career as a data nerd. At IBM, Paul leads from the front, helping to shape the strategic direction in a tech years like dog years world for the IBM technology unit. And all IBM software and hardware, sales, tech sales, and partner ecosystem. He’s leading journeys and upskilling programs. Coming up next, Paul Zikopoulos on the Conquer Local podcast. 

George: Welcome to Paul Zikopoulos on the show. We are video. Paul, we haven’t been doing video. This is, we’ve done 250 episodes of the Conquer Local podcast. And you’re joining us as we’ve transitioned to video and you look great. Thanks for joining us all the way from Oshawa, Ontario. And you know, I’ve gotta talk. In the intro we talked about you’re on “60 minutes”, you didn’t really start out to get into this AI world and be you know, a self-proclaimed data nerd. You know, how did you end up here? You don’t really have a technical background so I’d love to understand that for our listeners.

Paul: Yeah, I mean it’s been an interesting journey. And I have to give a lot of credit to my employer, IBM. I’ve been with them for 28 years and people often ask me 20 years, that’s a long time these days. But I’ve always been able to enhance my skills. I love to learn and that’s simple as that. So would I have thought I’d go from never taking a computer course to writing 21 books on technology? Nope, and that’s a credit I think to the grit that I brought to the employer and what the employer gave me in terms of opportunity. But, so there I am and I have to be candid with you, how do I get started? I just applied for a job in document writing, information development, ID, at IBM. I get hired for like 32 grand a year. And I think I’m rich ’cause I’m in university and I’m broke. So I just take the job, right? I was just like, “I just need to get a job.” And then I got in there and just started to learn how to communicate technology and at the same time learn the technology and then start to communicate it. And that was really the start of it. And I think in today’s kind of learning economy, and that’s what I will call it, no time in history have you ever been able to pivot or learn something about anything at any time. So there’s really an opportunity for everyone to get involved in whatever they wanna do. And that’s how it started. And from there it’s just showing up every day with a thought process that says, “Learning never ends and I’m gonna learn more today than I did yesterday.” And that adds up over decades. And here we are.

George: Well Paul, super inspiring as well for those that maybe need to polish up on their learning skills or maybe are feeling a little bit like they’re getting left behind. And I hear that in listener comments from the Conquer Local listeners all over the world. Sometimes I get these comments on LinkedIn that come in, messages saying, “You know, I really have to get better at learning and I have to understand that I don’t wanna get passed by.” Like, and I feel that myself on a day-to-day basis, as I work in a tech company too and there’s a lot of really smart young people in here that quite frankly, scare me into you know, polishing up on those learning skills. Is there any advice that you could give to the audience around how they could get better at that?

Paul: 100%, well let me just ask you this question. You look like you work out, so if you went to the gym and you met a personal trainer and they said, “Hey, if you work out for the next two months and follow my program, you’ll never have to work out for the rest of your life.” Would you think that person was crazy? Of course, you would. So why do we think that our skills in learning suddenly stop? Learning never ends. In fact, I would tell you that learning begins, once you leave university is where real learning begins on your shoulders. And I think the example I will give you is Bradley Cooper. If you know Bradley Cooper from the movie “A Star Is Born” with Lady Gaga, he’s this drunk, washed-out, kinda country singer that discovers Lady Gaga. Well everyone knows the movie, what most people don’t know is Lady Gaga demanded that he sing everything live and no auto-tuning. So this is one of Hollywood’s most sought out actors. He spent three and a half years taking piano lessons, singing lessons, guitar lessons for this role and it’s a remake of a movie. They filmed it in 42 days. So I think if Bradley Cooper spends three and a half years honing his skills for a movie, we should be honing our skills every day as well. And hopefully, that kind of analogy fits. If I told you you didn’t have to work out for the rest of your life, do you think you’d stay in shape? And the answer would be no. And the same’s gonna be with your skills. And you add to that the fact that tech years are like dog years, and technology is affecting everything in our lives. I like to say that technology affects if we live by die or try these days, then we’re gonna have to keep upskilling. And it becomes a culture of never-ending learning.

George: Well, I’m inspired. Like that inspired me. Who wouldn’t want to be Bradley Cooper? Like that’s very inspiring. So artificial intelligence-

Paul: Can’t promise you’ll make what he’ll make, but.

George: So artificial intelligence, buzzword, you hear it everywhere. There’s a lot of debate around AI. I was just recently at a conference in Florence and my presentation was around sales and every other presentation it seemed, was about AI. So I said AI in my presentation just to tie in with all the other presenters. We saw technology that can put a statue of David in any situation that you want, was the example ’cause we were in Florence. But I’d love to hear from you vice president Technology Group Skills and Enablement, what the hell is it really?

Paul: Yeah, so you know, I’ve written a number of books on AI, and my last book’s called “The AI Ladder” and it was really this guidebook for business people to understand and move AI from what everybody talks about to actual real-life changes to a company. I think I’ll start with this notion, we are being overwhelmed by data, okay? So if I was to think about the amount of data that I collect and plot that as a curve, a data collection curve might be really steep. But a data understanding curve would be kind of flat. And the difference between those curves is what I call the price of not knowing. We can’t understand the data that we collect. And so if you’ve been in the analytics or the BI, business intelligence, game for a while you used to always hear about oh, go find the needle in the haystack. But with that kind of data collection curve, we’re actually looking for needles in a stack of needles. And so quite frankly we need, as humans, we need help. And that’s where AI can help us you know, sense, understand, see patterns, listen, hear, emote, and all these kinds of things. But it’s not magic. And I think that’s the biggest thing that everybody thinks that AI is just magic. And so if I had to give you a way that AI is different from everything else, I think I’d give you this type of analogy or explanation. When we write rules for applications you know, a developer comes in and they write some code and we create all these rules. But AI works different. In AI we don’t write rules, we give AI examples of the data. And the AI, so we feed data to the network and the AI builds its own logic. So we explain things not with instructions but examples. Now that is the fundamental basis of AI. And so whether I’m trying to detect whether a mole on my arm is potentially cancerous, I would give it lots of examples of cancerous moles and non-cancerous moles. Whether I’m trying to teach AI, whether or not this particular person that I’m underwriting is a credit risk or an insurance risk, I would give examples of insurance risks and credit risks and examples of those who are not. And the AI will figure the logic. So if I wanted to teach AI what the letter A was, now the letter A could be a cat and it could be how much is front end damage on an insurance claim, it could be anything. Like that’s the beauty of it, we just change the data. I would give it a whole bunch of As in all kinds of different fonts, thousands of different fonts. And eventually, the AI would say, well it looks to me an A typically has a little hole in the middle or an angle, it has an apex, it has a bridge across from each line, and it has two feet. And then that’s how we would discover AI. Now of course under the covers, these are all mathematical representations. But that is in a nutshell what AI is. Give the AI data and examples, and it will figure out how to get to those and predict those.

George: That is probably the best interpretation of that overused buzzword that I’ve ever heard so thank you for that Paul. It finally makes sense to me.

Paul: Much overused.

George: In today’s environment that we’re in, I have noticed that buyers have a tendency to be talking more about efficiencies and automation than maybe even growth, which is weird because for the longest time it was grow, grow, grow at all costs. But there’s a thing happening right now with inflation and we’re looking at you know, record layoffs happening. And I don’t even wanna use the R-word because I believe if you use it then it happens. But when I hear AI and hear the way that you explain it, how could we talk to a business owner about the way to get started with AI? Because there’s a huge demand there I find from customers going, “I need AI.” Do they really need AI or do they need what AI could provide? And I’d love if you could break it down, how does somebody get started with AI?

Paul: So there’s no question we’re in this hyperinflationary environment and it’s causing businesses to have to rethink. We are seeing revenue growth but we’re seeing a shrinkage of cash flow. And that’s because the costs of business are enormous, beyond wage costs but that’s obviously, especially in the tech sector. I think, today it’s November the ninth, and Meta Facebook just announced a 13% layoff of their entire course. That is massive. And that’s an AI company. So I think that the first thing you understand is AI is not magic and there’s no place for data science projects. You don’t do AI for fun. And I can tell you, I was at the forefront of the big data movement. I wrote the book “Hadoop for Dummies”. That’s very ubiquitous technology at the time with big data. And so many customers failed at what they were doing because they weren’t delivering value to the business. So if I put my business hat on or this is where I kinda come from, I would approach customers and I would say, “Look, we should really create two categories of your investments in this coming year and the challenging up-and-coming potential recession we’ll see. You will either spend money to save money or spend money to make money.” When you spend money to save money, you’re renovating the infrastructure. And when you spend money to make money, you’re innovating the infrastructure and your digital transformation. And in this kind of inflationary times, you need a strategy to spend money to save money. And then we can take those savings and fund or spend money to make money. And AI can be enormously helpful on either of those, spend to save, spend to make. And so I advise folks is to take some AI projects and throw them away. Now take your business needs, bring them to the table and let’s arrange them under three categories. Because this is how I ask everyone to categorize AI. You will use AI either to optimize the business, the process, et cetera, et cetera. To automate the business, the process, et cetera, et cetera. Or to predict the business, the process, and so on. And so if we start creating this two-dimensional model spend to save, spend to make, take these business initiatives that we have to do. So maybe it’s more frictionless customer onboarding, a better way to process insurance claims that are under $500 for front-end damage, and then we put them into those three pillars, optimize, automate or predict. You’re gonna have an excellent blueprint for which to get started and start to drive value of AI because you’ll be led by business and not by a bunch of data scientists that wanna go and run some algorithms. And that’s the best advice I could give.

George: Well, and I like that. And I also like business intelligence. You know, I work with a bunch of folks that do that work but I’ve found also that they want an outcome from the work that they’re doing as well. You’re really focusing this around three outcomes. Can we automate certain things, can we optimize certain things, and can we predict? And having that outcome focus then makes the use of technology, the investment in technology really just be a rational decision in my opinion. Do you find that as well?

Paul: Yeah. Yeah, 100%. And then the other little kind of turbo boost to that is for you to understand as businesses that data is gonna come from places you would’ve never imagined. And that is what’s feeding that collection data curve that I talked about. So it’s going and applying the business projects and getting them classified correctly and then opening our aperture of where the data will come from. And since you brought up Italy, I wanna give you like an interesting fact where you would’ve never thought, “Wow, data comes from there?” So I mean, we mentioned Facebook Meta earlier. They own the app WhatsApp, the messaging app. In fact, that’s 25% of the world’s population is on WhatsApp. Second most popular non-bundled app in the world, WhatsApp. Well in Italy, WhatsApp messages evidence is used to divorce nearly half of Italian adulterers. So the head of the matrimonial association for lawyers in Italy said, “We’ve seen adulterers using WhatsApp to maintain three or four relationships. It’s like dynamite.” So I’ll ask you, would you ever think that a law society would start advising folks to use WhatsApp evidence in the discovery process and natural language processing to categorize for an at-fault divorce country? Never. So that should give you an idea of just how wide the aperture is for the data. And now if we focus it on the outcomes for the business, we can get somewhere way more quickly than the other approach.

George: No, and that is an interesting analogy. I’ve got one that just popped up the other day. My timeline on Google Maps told me that I’d been to the local watering hole by our house 248 times this year. Well, hopefully, it’s not this year. I don’t know what the timeline was, but I saw the 248 and I was like, “Whoa, that’s a lot of time.” But you’re right, the data can come from everywhere and that is an interesting analogy, that’s for sure. And we all hear more stories about that. There’s one when people hear the word AI, and there’s been the odd thing, Elon Musk came out one day and said, “Oh, we gotta be really careful with AI.” And then others are like, “No, we need more AI.” And then I think back to you know, that movie with the robots that took over and you know, the very famous franchise of “Terminator”. And the question is you know, when will AI take over our jobs and when will we fight against it? Like, do you hear that as well in your role?

Paul: All the time. And it’s more the fear and a lack of understanding of what AI is today, right? I mean, that’s what’s happening there. So you talk about movies, you think about “Ultron” in the Avengers movie where they created this AI and it was to keep the peace among humankind. It figured out that humans are responsible for disrupting the peace so it concluded that AI has to kill all the people, right? And you’re like, “Oh!” And so that makes people get scared. But that kind of AI is best left for the movies, quite frankly. The danger in AI isn’t having these kinda AI-powered robots with autonomy that will kinda take over our lives. To me, the scary part should be the data that we’re using to train the AI. And I alluded to this earlier, and not to sound Dr. Seuss-ish, but AI is going to decide whether you live by die or try. And so if it’s gonna do that then we have to move into this new world where it’s not just about how accurate is the AI but it has to be accountable AI. And while that’s a whole podcast on its own, I do hear that. And then I do hear discussions about jobs. Will this replace my job and will it not? And you know, I’ll have more to say about that if you wanna talk about that but you tell me.

George: Well Paul you know, we’ve covered off a few really important items that have helped me to understand this. And it’s not just the fact that I was at this conference recently and everybody was talking about AI, we’ve been hearing about this for years now. And we wanted to bring on an expert like you to the show to help our audience of Sales professionals all over the world kinda understand more about it. And you’ve done a great job of setting the foundation. I love the fact that we talked about how to get started with AI, around that idea of automation, optimization, and prediction. But I do wanna dig deeper into you know, maybe the dark side of it. You know, we get a little paranoid almost around it. And I know you are definitely the professional that can help us to understand it more. So I’m gonna call in Audible. We’re doing two episodes with Paul. We’re gonna come back next week with another episode and we’ll talk more about AI. Will it replace our jobs? AI, is it mankind versus robots? Paul Zikopolous vice president IBM Technology Group Skills and Enablement. Thanks for joining us on this week’s episode of the Conquer Local podcast. And we’ll see you next week. 


George: The biggest takeaways from today’s episode with Paul, we can write rules for code. But in AI they don’t write rules, they give examples and explain things with instructions. And I like how Paul breaks down our questioning using layman’s terms. For instance, he highlighted examples of cancerous moles and non-cancerous moles and the statistics from their findings and the research. And that’s how they train the artificial intelligence. Not only did we cover the accuracy about AI, but on how to build accountable AI and discussions around what our jobs might look like. And we’ll be getting into more details around AI and how it might affect the workforce in next week’s episode. Stay tuned for part two coming up next week. And if you liked Paul’s episode discussing artificial intelligence, let’s continue the conversation. Check out episodes 518 and 519 around digital accessibility with our friends from AudioEye, Alisa Smith and Ty D’Amore. Please subscribe and leave us a review wherever you hear the Conquer Local podcast. And thanks for joining us this week. My name is George Leith, I’ll see you when I see you.

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