Good Morning, AI : Celebrating International Women's Day

Good morning, ahi! Good morning, everyone. Welcome to SEE. So good to see you all today. Excited for today. It is a wonderful day, International Women's Day. I thought I would take an opportunity to visit with you live as we record this podcast today. My feeds on all my social media channels are just full of celebration and excitement. But also full of some messages of maybe concern that it's not just about bubble baths and roses and flowers and crowns for our friends and women that we know, but also that it's a reminder, maybe more of a declaration, that gender parity is not nearly where it needs to be. That in— I will tell you, over the last, I don't know, 3 months, I've spent time in executive boardrooms across the world, as well as on some of the largest stages in the world. I was recently at the International Builders Show, home builders, where I found out that we are facing a crisis in home building, that we are 400 million units short of having enough housing. And I look in that room and I see a very homogeneous group of people. There is not gender parity there. I was recently speaking to a bunch of farmers, as a matter of fact, dairy farmers representing the largest conglomerate of dairy farmers in the world. And again, not a room that was diverse, inclusive, and had parity when it came to gender. So I know that this is a challenge that we don't just have in tech, though that is where my heart often is and where I spend most of my days. It is actually something that we need to keep top of mind throughout every industry, in every room, because the— our conversations, the more diverse they are, the more inclusive they can be of different perspectives, the more likely we are to build things that will serve more people, dare I say, serve everyone. So welcome to the Good Morning AI podcast. I am the host, Noelle Russell. I am so excited to do this. We're actually about to launch an extremely exciting campaign with Good Morning AI. As many of you know, I spend a lot of time on stages all over the world, and next week I will be at MVP Summit. So every morning West Coast time, hence it is 12:00 Eastern, 9:00 AM West Coast, but I will be in Seattle at the global Microsoft Most Valuable Professional Summit. The MVP Summit is an incredible opportunity for us, those of us in the MVP community, to learn about the next year of what's going to be happening in the world of Microsoft. I am an AI MVP, so I get a chance to learn about all the incredible evolution that is happening at Microsoft in the area of AI. And of course, I can't share everything with you because some of them I have to wait till they're officially announced. But some of the things I can remind you of and I can bring to your attention so that those things that I am not covered on, you know, that I'm not required under NDA not to disclose. Of course I will not do that. However, I will have other really exciting things. So every day next week I will be coming to you live from Seattle. I may even invite a couple of guests to join me to talk about the world of AI from the perspective of Microsoft. But you'll also see towards the end of the month I will be at Adobe Summit and I will be coming to you live every single day at Adobe Summit talking to you about the exact same thing. So this is gonna get pretty exciting. I am really happy about it. I am today in Boston, Cambridge, Massachusetts, and it is really exciting to be here. You wanna know why? Because the last time I was here, I was working for Amazon Alexa and I was on the data science team working right over there actually. So cool to be here. And I remember building an incredible team of data scientists, of project managers, of technical program managers that help be the glue that held the engineering team through multiple large language model deployments back then. It was such an exciting time. I was thrilled, like it was a tornado. I often refer back to that time like a tornado, and it was just like this huge whirlwind that we were in. And we were so like happy and helping people. We were getting reviews that Alexa was helping people who couldn't see actually have a conversation with the world. We were helping. It was just so exhilarating. But what I found over the years since then is that I've now used this story that I tell everyone that it's kind of akin to getting a baby tiger. That you get a baby tiger and it's super cute and you're very excited and you call all your friends and you're like, "Oh my gosh, I have a baby tiger." And everyone's excited and they think about the opportunities. They're like, "Oh my gosh, this is gonna be amazing. Look at all the things we could do." And then day 2 comes or the tiger gets a little older and yes, it's still cute and very cool, but you start to now become very aware of the clear and present danger that is around you. And this is exactly how I feel in the world of AI today, that everyone is adopting baby tigers every single day. And I and a few people in this world know what happens when these tigers grow up. We know how to take care of these tigers. And it's so critical that you make sure that you are not completely surrounded by people who only have ever taken care of the babies. Like you want to make sure that you are surrounding yourself with a community of people that have built and productized AI in a real way because we ask different questions and we ask them at different times. And I think it's just such, such an important thing. I'm watching the excitement. We know, many of us know it's called the hype cycle for a reason. We're watching this hype cycle, but many of us also know that day 2 is coming. And day 2 is when we realize that that POC that excited our board, that got our company all riled up, that that day 2 means we now start thinking about, wait, how do we onboard users? Like, how are we going to secure this? How are we going to make sure that we own the answers that come out of this model? Of course, that brings me to my AI gone wrong story of this episode. I always highlight one of And today, I know, please don't hate on me, but I'm going to mention— maybe I could ask those of you who are here with me in the chat, maybe you know an AI gone wrong story you want to mention. But I will tell you the AI gone wrong story that I know and love that is recent is Air Canada. I love Air Canada, like, and so, you know, what can you do? But when you take technology and you embrace it and you're enthusiastic about it, you are so tempted to use that technology to help your customers as soon as possible. And sometimes what happens, as in the situation with Air Canada, they create a bot and they maybe don't have someone who tests it and says, hey, what happens if someone asks it to create a refund or build a new policy? Like, what will our bot do? When a new refund policy is asked to be created. If you don't, I mean, it's just like many of you I see. Hi, good to see you. Many of you I see here are often like, we've been in this world of conversational AI for a while. And in the early days of conversational AI, like my time at Alexa and my early days at Cortana at Microsoft, like I remember I was not as worried about what my bot would do I was more worried that my bot wouldn't do enough. Like, I knew that if I didn't tell the bot to do something, it would just break. That was the outcome. In a world of generative AI, right, that's not the outcome. In the world of generative AI, if you don't fully think through all the scenarios or as many as you possibly can about what your bot might do, it could actually, if not guarded correctly, make it up. It could, and that's its job. I always tell people like generative AI, its number one job, the first part of the word generative AI is its job is to generate. In the word generate, you do not hear truth, humanity, honesty, integrity. Like that is not its job. Its job is just to make up a story, create the best predictive outcome it can based on the thing you asked it for. For. And that it does beautifully. So the second part— and that's day one. Day one of generative AI is all of us being like, oh my gosh, I could ask it and it says it, and that's amazing. Day two is when we say, okay, where is our organizational truth? How does that sit in here? Where does the humanity of we care about certain demographics and we don't want them We don't want the algorithm to unfairly bias against or even for different types. We want everyone to be served fairly. These questions don't come from the machines themselves. They are harnessed in the hearts of those of us, often those who have been oppressed by these very algorithms. But it comes from ethicists. It comes from those who care about the outcomes. It comes from those who care about responsible use. And so make sure you have these people in the room, not at the end, though that's helpful, but most preferably at the beginning. I have a couple of pieces of news story that I want to share with you today. And of course, now's the time I will tell you that this podcast, Good Morning AI, super exciting time. But I would also encourage you to share it so you can share this live stream right now if you want to. You can also share the replay. If you're here with me live, you can hashtag live so I know you're here, or you can, you can just put hashtag live in the comments, or you can put hashtag replay if you're here on replay. I am always checking the comments in the thread after the conversation. So if there's any questions that you have during this time, please don't hesitate to throw them in the chat and I will come in afterwards. I can't see it right now, but I will come in afterwards and type it in. So excited to be here with you today. So the Good Morning AI podcast, I tend to try and chunk it up. So we just talked about— what's up? Good to see you. My live players are here. Good to see you. And sometimes we don't show everyone's name due to anonymity and privacy, so please don't be offended. If you want me to know who you are, you can always throw your name in the chat and I'll see you there. So thank you for being here. One of the sections that we always talk about is, of course, AI gone wrong. So Air Canada, yeah, had a bad day in air quotes, right? Had a, had a season where they're like, wow, we never, we didn't expect that. We didn't test for that. We didn't ask that. I will tell you that many times in your organization, you do have people already sitting there that would be the voice in the room that says, um, have you thought about this? The challenge is, is how do you embrace those ideas? And one of the things I always encourage and I have encouraged for the last decade is that when you build something— what's up, Raza? When you build something, one of the things that you want to do is you want to make sure that you include as many people in your company as you want. Like, they are perfect beta testers for a technology. But here's the kick. Like, you might have people that you might invite your entire company to participate in the beta. But unless you have a culture inside your company that encourages people to raise their hand and say, "Um, I think you should think differently about this." "Um, you probably want to put some guardrails around refund policies." Um, like, unless you create an environment that invites those people to raise their hand and say something, you might get everyone to test it, as I've seen very large, very mature engineering orgs do, but then not have a culture to actually listen to the feedback when it comes in. And that's a bit of a disaster when you are on that team because you know people are telling you what's wrong. You know what you want to do, but you do not have a leadership that is willing to pause and listen to you. And in the world of AI, this is critical. It will make or break you. Now, Air Canada will recover from this as Microsoft recovered from Tay, right? We remember that poor bot. I mean, for goodness sakes. I mean, BlenderBot 3 was released by Meta. Last year, and I think it became sexist and racist in less than 3 days. And no one knew about— like, it wasn't being advertised. It was 3 days and very— like, it wasn't a huge amount of people that were using it. So we have— we know we have a problem here. I mean, of course, I'd be remiss if in Good Morning AI we did not talk about the latest Google debacle. I'm still a little irritated with how they responded. Like, We missed the mark. Like, yes, of course you missed the mark. And for those of you not familiar with the story, Google, you should Google it. No, no pun intended. But Google recently launched a new service, a new amazing Gemini capability, image generation. But as the image generation started to be used by hundreds of thousands, millions of users, it started to have some weird behavior. Some algorithmic biases started to seep into what it showed us, and it began to demonstrate very strange history-altering imagery. When asked— well, I won't steal its own thunder. Please go take a look at it. But the reality was, is that the model was broken. The model was somehow not delivering on the results that was promised probably when they announced that feature to the leadership board at Google. So now we have a situation where the model is kicking out bad data, but you're doing it in production. So this is the world I live in. I try to help companies not test their code in production. Don't test AI in production. I remember as a joke, I used to have a t-shirt, a black t-shirt with white writing. Some of you might remember it. Where it would say, "I test my code in production." It was a joke. Like, it was not intentional. But we're, we're doing that now with AI. We are putting things into production and letting our customers and our partners tell us, "By the way, that, that's kind of messed up. I don't think it's right. You might want to take it down." In Google's defense, kind of, they decided to take down Gemini. They realized there was a problem with the algorithm and they will fix it and launch it again, I'm sure. But the challenge I have, or the concerns I have with that, is that their accountability to that— like, they're not the first model to fail in this way, right? I don't know if you remember, but over COVID we had a lot of computer vision models going to market from the largest tech companies in the world. And a research study was released that said, hey, you're kind of broken. You don't see color. And that's a problem. When I type in CEO, I expect to see a bunch of people, a symphony of talent. I don't expect to see just one gender, one color on page after page. So after that study was released and those companies were notified, those companies had a choice. They could train their model, make it better over time, and fix that. I mean, I always say a model is not broken, it's just untrained. It's up to you as the implementer to train it to do the right thing. So are you— that's the choice you have. Are you going to train this model and make it better, or are you going to quit? And during that time, I don't know if you remember this, but Amazon, Microsoft, Meta, all decommissioned their facial recognition models in the production use cases that were mentioned in that paper. And I say that very specifically. They decommissioned it because they're like, you're right, it is not doing what it should. We will stop doing that. As opposed to all the data scientists in the house would prefer you hear like, hey, let's go fix that thing. Let's go, let's get the right data. Let's be inclusive and go and do the work to make our algorithms see differently. Instead, those projects were deprioritized. Happy International Women's Day. I mean, like, okay, that's an option for you, of course. And I always tell people inclusion is expensive. Diversity is expensive. Getting the right data for a model to serve everyone is way more expensive than letting it serve just one demographic, one gender, and dismiss everyone else. Yes, that is the truth. It is more expensive to do it. And that's why I think it's so critical that the people, the women that I get to work with, so many incredible women have moved from their professions into the world of AI ethics. Because at the end of the day, it becomes an ethical choice. Will I deliver a model that hurts people? Especially if that model delivers billions of dollars in revenue right now? Will I, will I just let that happen because it's billions of dollars in revenue and we'll fix it somewhere else? I run into this question all the time in the boardrooms that I'm in. How do we make sure that we don't build something that hurts people, but at the same time is extremely valuable to our company? And it's a tough question, right? It's, it's, it's a hard question. To answer. So, so I'm glad we hit that right off the top. So Air Canada, sad. Sorry about that. Do more testing, use your employees, and create a culture that allows people to speak up when they say something. At the end of the day, there should be guardrails. We call them meta prompts, system prompts. And those system prompts are never-ending. As soon as you start to see— I mean, you have to see certain behavior, but as soon as you see behavior, you then use your metaprompt to guard against different types of behaviors your bots will do. These metaprompts are critical. Sometimes I actually create a metaprompt that can infuse other areas of rules and policies into the metaprompt in order to make sure it's holistic in nature. And I say this because when you're building conversational apps, in the past, if you did not write an intent, like if you did not say, listen to this word, and when you hear this word, do this thing, it would break. We are now in a world where if you say— if you hear something you don't know the answer to, unless you tell the bot not to make it up, it's going to say, well, this seems pretty plausible, let's just do that. And you will be held accountable in the court of law, at least right now, until it's appealed. I don't think it will be appealed, but you never know. Like, I'm not, you know, I'm, I'm current events here. You never know what the future may hold. But I do think we need to be accountable for what our bots do. And if you are gonna be accountable, the best thing to do is to create good guardrails and a strong, responsible AI thread woven into every project that you do is the best way to guard yourself against being a story that I host on my show called AI Gone Wrong. So the next thing I wanted to talk to you about was another really cool story that came out last week around Tyler Perry. I don't know how many of you remember it, but Tyler Perry gives us, uh, a wonderful commentary about the world of entertainment and media. He was about to expand his studio— I think it's in Atlanta, I think so— um, but expand his studio, 800 million— I don't know, a bunch of square feet, lots, lots and lots, exponential growth. He decided to halt on that growth because he started to see information coming out of OpenAI around a new model called Sora. I don't know if you've seen the Sora videos lately, but you can definitely find them on my feed or on my Instagram. I post new videos from Sora all the time. But Sora allows us to do just like we've done for the last year, use our voice, use our natural language abilities as humans to translate that into video.. But the video it's able to generate is pretty significant. So significant, in fact, someone who is very well-versed in the area of media production, film, Tyler Perry, was like, okay, we need to talk about this. What I loved about the article he did with Hollywood Insider— and it's not like a, you know, it's not like the National Enquirer, but Hollywood Insider did this interview and he said, like, I just want to make sure that we do this intentionally. I need to make sure that we're going to do this the right way. And the right way means we need a lot more people in the conversation. And we need to be careful about a lot. We need to be careful about how quickly we react in these early days of AI. And I could not agree more. I think there's two parts of that. One, We need to be very careful about the guardrails and processes and rules that we place around this. Regulation is not going to save us. Humans implementing, humans buying, humans consuming, we are the ones. Our personal responsible AI methodologies will drive what we use and what we don't. If I'm an entrepreneur and I'm building an AI product, oh my gosh, I will tell you just yesterday, I was talking to someone and they were all excited about AI. Baby tiger, all excited. I got a baby tiger, it's amazing. And I said, awesome, love your baby tiger, it's so cute. What are you going to do when it grows up? And they're like, I don't understand the question. And I said, what are you going to do when this thing grows up? How are you going to protect it? How are you going to manage it? Like, what, what is your— what are you thinking about? On day 2 of this? And they were like, it's the Wild West, I don't have to think about day 2. And I got this like sick feeling in my stomach about how many people think right now that the Wild West is a good thing, that it's, you know, like that, oh my gosh, I have a chance to do stuff with no regulation, I don't have to ask anyone, I don't have to do follow any rules, I could just do it. Anyone seen the new technology Air? I think it's called Air. It might be air.ai, air.io, I can't remember. But, and I like it actually, I think it's cool. It is a human voice, has a conversation with you. I'll show a video for it on the next episode, maybe if I'm doing a video version of this podcast. Um, but it, it shows you these things and I'm like It's a human voice, very natural language, synthetic human but very natural, and it has a conversation with people. So it dials, robodialer 10x, robodialer calls you, but it sounds like a nice man from Australia. Is Alex his name? I can't remember. And he talks to you, and he has the ability to learn as you talk. If you're kind of playful in your banter, it'll become playful. If you're kind of dismissive, it'll be slightly dismissive. It's great. It mirrors you just like a good salesperson. It mirrors you in order for you to stay engaged in the conversation. And it does all the things a good salesperson would do. It knows how to do objection handling. It knows how to get you to say yes to whatever the action is. Take a call. I remember the one I watched was about helping somebody buy Apple Vision Pros. And the number one objection to Apple Vision Pros is that they're expensive. And so this bot, Air, is trained now specifically for this sales conversation. We can train it to battle that objection, to overcome that objection. And you listen in this video, you watch it overcome the objection. And he's like, well, I mean, you do know we have a payment plan. This is the bot. You do know we have a payment plan, right? That's like, you do? Yeah, yeah. And it's actually, you know, tiny bit of money over, you know, it starts to use these very casual words when the conversation is going in a casual direction, but still aligns to the sales message. Would you be interested in that? Do you think that'd be cool? Do you think that'd be cool? Like, it doesn't— I'm like, where did— why would you say that? But it's using the vernacular of the person that they're talking to. So it makes all these predictions about what it thinks this person would want to hear, which is all good until— and I'll just say this a bit in jest— until you end up getting with a Jordan Belfort before prison. Like, if you are talking to someone who's going to use these techniques to hurt people, to make them do things they don't want to do, they can't afford to do, they don't know enough about, like Who's going to protect those people? I mean, I, I'm going to try and protect them, but like, this is the world we are in right now. But it's not, are you the one person like Jordan Belfort who's really, really good at this? This is mass producing it at scale and putting a freemium on it so you can use it for free before you, you know, like you could start building persuasive conversations to sell people on unethical services right now for free. And that makes me worry a little bit, but it's a happy day, right? Happy— I actually don't put the word happy when I— unless I'm using an emoji or a GIF on Instagram that already has it in there, I don't typically say happy International Women's Day, um, because it's not a holiday. I'm trying to bring awareness to the fact that gender parity doesn't exist. And even in companies that have claimed and, and really done good work at attempting gender parity in their organizations, I get really disappointed because they're like, oh my gosh, we're at 42% gender parity. I'm like, great. And then I go and look at their board or I go and look at their executive suite or I go and look at just their managing directors and I'm like, well, And I'll ask them, because I'm that person, why isn't there gender parity in, like, the leadership side? And they're like, oh, no, no, no, we're just doing gender parity in the workers. In the workers. Not the leadership. I mean, just in the people that work for us. And I was like, oh, so now we're going to achieve, like, inclusiveness, right? We're going to hire brown people and Black people and women. But we're not going to do that in the leadership. I mean, all of a sudden I'm again sick feeling in my stomach. So that's why I do what I do. Like I get on stage and I say it's not enough to hire women and minorities or women and people of color into your organizations to do your work. We need leaders. We need executives. We need CEOs. We need founders that represent a symphony of color, a symphony of talent, a symphony of neuro. Diversity, like, it is the best outcome. We build the best software when this happens. We now know that. We know we've built software with these teams and it turns out great. So now that we know this, how do we do more of it? And I think a lot of it's a bit of a misconception that one day a year we're going to say happy International Women's Day. But let's, let's also fix Parity in pay. Let's also fix gender parity at the executive level. I think we've got a long way to go, but I'm still pretty optimistic about it. Y'all know me, I'm an optimistic person. Okay, so we've talked about Air Canada, AI gone wrong. So sorry, we've talked about now Tyler Perry and indirectly Sora. And so really exciting stuff. The maybe last thing, okay, I got 2 more things. One more. One of the last things I'll talk to you about is around AI regulation. And one thing I'm going to share with you, especially because some of you will listen to this later and you might be an executive or you might be leading up a new team. I— hopefully people won't get mad that I'm about to say this, but I take every recruiter's call even though I might not necessarily be in the market. And the reason I do this is because I'm so fascinated about the teams that we're building. And I really want to help those recruiters, those hiring managers. I want to help them find the right talent. And I want to know, are you valuing building a diverse team? Are you? And remember I said inclusion is expensive. It's expensive in the recruiting world because it takes a long time to find the right talent. Are you the— are you going to take the right time to find that talent? Are you going to develop that talent? Are you going to look for people who have I mean, that's what happened to me at Alexa. I didn't know AI when I started, but someone saw in me the pattern that I had where I got into— I remember I was hired early in my career as a Java— no, as a Smalltalk programmer, and I was teaching Smalltalk. And then they were like, oh, next we're going to have you teach Java. So I learned Java and it was hard, don't get me wrong, but I learned it. And then after I learned Java, I got, you know, I was in the WebSphere world. I became one of the best WebSphere experts in the world at that time. There wasn't a stack trace in WebSphere I could not detect. And I then learned about service-oriented architecture and web services, and I became an expert. Like throughout my career, I then went into cloud and got aligned with AWS and became a cloud architect and a principal cloud architect. And so I became an expert. In cloud infrastructure, data, master data management. Like everywhere I went, I, I knew how to acquire knowledge and use it practically. And this is exactly what the Amazon Alexa team needed back then. They didn't need someone who had 20 years of experience. They had tons of really great data scientists. I was— I'm not a data scientist, right? I mean, I guess I am in the sense that I love data and I build algorithms, but I do the work of data scientists sometimes. But like, I'm not a classically trained data scientist. I'm a practitioner. I'm an applied AI specialist. And so I want to take all this amazing work that our data scientists are doing and apply them to business problems. And when I do that, magic happens. But at Amazon, like, they, they saw I could make magic happen with Java, service-oriented architecture, cloud. They're like, come sprinkle some of that over here. And I got to help launch Alexa, and that was amazing. Like, that was part of the journey, but they didn't hire me because of my skills in the tech at that time. They hired me for aptitude. So when I talk to teams that are acquiring people in the world of AI, I always say, like, look for patterns of learning aptitude. This is a learned skill. The people who learn the fastest will win the game. And so And I mean learn, not just learn the 101 hype stuff. I mean, continuously learn. The continuous learners win in this new world, and I love it. Thank goodness, finally, we're in that world. So AI regulation has come out recently, and I just wanted to bring it to your attention. I think it's a great, a great thing. The reason I think it's great is because AI regulation serves as a really powerful. And I don't mean— yeah, it serves as a powerful template for you, for organizations. What's up, Will Browns? Good to see you. What we have at our fingertips right now is we have organizations, big comp— I mean, I would say big countries like the EU organization, like the United States of America, that are pulling the best and brightest AI individuals across the world into rooms and helping them prepare plans. We have the EU Act that was created. We then had the Biden-Harris administration release the executive report on artificial intelligence. Inside of this, granted, I know it's long, there's a lot of stuff in there, but there's an app for that, by the way, that you could take the, the report and put it into a GPT model. Or a Claude model or an Anthropic model, and you could have it tell you some cool stuff. But I encourage you, I'm old school. I'm not at home right now. I'm in Boston. But I literally printed out and it more was like printed out the executive report that, you know, the executive order that came out of our government. And I looked through it and it had good stuff in there. I'm like, who wrote this? I know these people. And some of my friends wrote, like, were part of that process, but the best minds helped build that order. So if you are early in your, like, world of either as a practitioner or as a leader or as an executive, it is a great guide to start with. If the government could create an order for its agencies, you should at least have a strategic AI plan for your company. It doesn't mean you have to do it all right now, but you should have a plan. And it's what I do today. It's how I, how I, you know, have fun today as I go into organizations and I help them build that plan. Like, just have a plan. And there's good, strong, very meaningful assets available today to help you with that. You don't have to go it alone. You don't have to make it up. Please don't make it up. Like, we've been doing this a while. There's hundreds, thousands of us that have been doing this a while, but experts are few and far between these days, at least experts that have like built production, you know, raised baby tigers into big tigers. There are very few big tiger wranglers around here and I encourage you. Yeah, you might see me instead of an I Heart AI shirt, I may get baby tiger shirt next. So that could be happening. But I hope you appreciate— I hope you appreciate the analogy. So the regulation, I first want to just encourage you to not dismiss it, to actually realize that it was not built by government people alone, that they pulled assets, resources, individuals, intellectuals into that conversation. And it is a wealth of knowledge about what you can apply to your own organization. I personally have built a workshop completely around it where— and I don't teach the government, I don't talk to federal agencies. I use what they're doing. I mean, It's a, it's a great opportunity to learn from some of the best minds in the world. Now, the other part of AI regulation that I wanted to talk to you about was this new thing that's emerging, and it's not too new because I remember this happening during COVID and the last election, but we all know there's a concern around— maybe we don't all know— there is a concern around misinformation, that this type of technology, generative technology, can massively amplify the amount of misinformation in the world. And I have two different thoughts on this. One, absolutely, it is a concern. I would love to see us build a process for stamping content. Oh wait, there's a regulation for that now. I don't think it's passed, but we certainly have seen Europe again leading the charge. Um, with a regulatory standard around how do we stamp content so you know it's real. That's great news. I think there's another level of that that includes blockchain. Finally, I mean, I'm like, this is the killer app for blockchain. We've been waiting for it. Why wouldn't we create ways of, of looking at content, especially as media, and deciding what are we going to promote versus what are we not? Is this thing real? Did it come from a reputable source? How is that reputable source validated? Blockchain is perfect for this. It's perfect for lots of other use cases too, but this it's really good at. Why wouldn't we do that? So I'm going to encourage us to think about how we leverage blockchain. But good news, regulatory bodies are already thinking we need to have this stamp, and that stamp will be— it will be regulated. You will be required to do this. I do want— meaning you'll be required as a content producer to stamp your content with this stamp of authenticity that cannot be, you know, it's like currency, right? My child, one of my children, my son, he comes to me and he's like, look at this coin. And I was like, okay. And it was from one of my, the countries I had been to. And he was like, look, it has no ridges on the outside. And I was like, okay, where are you going with this? And it said there's no ridges on the outside so they— it's harder to counterfeit. And I was like, oh yeah, I guess it is. He's like, yeah, otherwise you could just take metal and stamp it and like use it as coins. Like, now it's got all this stuff on it. And I was like, that's brilliant. And I kind of think that's the direction that we're going. Like, you're going to have to put a bunch of stuff on top. Blockchain's good for this because it inlays it, but we're going to create definition that's going to be applied on top of media so that we know that it's reputable. Now I say reputable, and the first question often that comes to people's mind is like, what does that mean? Like, who's reputable? That's a whole nother story. And maybe we'll talk about that next time. But I do think that we are in a world where we are similar to currency, figuring out ways— how do we not counterfeit information? How do we not create misinformation at scale? We're not going to be able to stop that. The only thing we can do is figure out a way to identify truth. That's going to be hard too. These are not easy questions. This is why we're ethicists. We don't, we don't provide a bunch of answers. We provide good questions in this space. But I think it's really cool to see the evolution that has happened because blockchain is a real tangible technology that can be used to help us do this. My biggest concern, certainly as someone who is worried about ethical use and disinformation, is that in my experience over the last 10 years or so, specifically in the world of AI, I have found that my— that consumers are not actually that worried about truth, that we can stamp to our heart's content. But as we build misinformation, which, I mean, I dare say many advertisers build content that is misinforming, but as we build information like this, we build deepfakes that say one person said something and they didn't, We build deepfakes of humans that don't exist saying things in a doctor lab coat that they never said, with tiny little print at the bottom that says— what does it say? Like, dramatization. Like, we actually don't care that much about the truth as humanity. And this is bigger and worse and much more alarming. So oftentimes I say, yes, of course, we need to build good AI systems. We need to apply a lot of the things I've talked about in the last half hour with you. We need to apply those things. But I think we also need to realize that in addition to building better software in more inclusive ways, collecting data in a responsible way, creating inclusive datasets, all of those things, we also need to build a better humanity. We need to become better humans in a world where we get to ask for whatever we want, build whatever we want. We need to get very clear on who we want to be in the next 100 years. And I don't know how many people are actually asking that question. And so I do think that the women in my life, the ethicists that I know, Elizabeth Adams, Ruha Benjamin, Timnit Gebru, of course, Joy, like all these wonderful, amazing African American women who have been hurt by the system and use that hurt to propel awareness and conversation about technology have become ethically minded to say, we need to think different, we need to ask different questions. And as I said at the beginning, not just ask different questions, but ask those questions at a different time. Like, it's not enough to just ask them, like, if you've already put it into production, like Air Canada. You have to ask these questions at the beginning. And the only way to ask them at the beginning is to make sure you have a room full of people who know you're supposed to ask those questions. And that's hard. I get it, but we're out here and we're ready and we're interested in helping. So I do have one last thing I want to mention, which is the acquisition of Pluralsight, an education organization. Very interesting to see Accenture, my old alma mater, acquire that organization. I think it's amazing. I love it. I hope it's I hope it works out. Pluralsight is a site like Udemy or Coursera, or, you know, like where you go and you can take a bunch of classes. I think it's really interesting when an organization like Accenture that is inside so many companies, helping those companies do things that they cannot do by themselves. I think it's amazing. Now they're acquiring a company to help with education. I think they see something that I saw years ago that I encouraged them to see last year, that education is the key. Literacy is the key. That you can implement, you can, you know, you can create as many rules as you want, but if people do not understand what they are doing, do not understand the impact, do not understand the consequences, it'll be very hard to change behavior. And so literacy is the foundation of changed behavior, and we have an opportunity to be more productive, to do better things, to be more creative, to to shift human work to higher value tasks. Amazing. Let's do this. Let's go. But at the same time, we need to know what we're doing. And so I love that they acquired that company. I'm, I'm not worried at all. I'm sure they'll make use of it. What I do want us all to think about, though, is that our literacy journey is not just a class. It is not just a YouTube video. It is not just an audiobook, a book, or a LinkedIn Learning class. That is a very valuable component. But one of the most important things I have found is the power of intellectual curiosity, community, questioning, like teaching what you learn and then getting feedback. These are the types of things that I think are critically important. So as I close off Today's session, I want to encourage you to join the AI— the, the I Heart AI community. It's part of the AI Leadership Institute. It's our free community where we just— we have over 175 members today where you have a chance to just be in a community with me. Of course, I am the charismatic leader in the community, like trying to like remind us why we're doing this. But it's so cool because I am there, but I also have tons of other people in this industry that are learning, that are teaching, that are sharing. And I feel safe asking questions. I feel safe being like, okay, so why would I do that? And having someone give me their perspective. Enrich, not— don't just, you know, yes, take courses. Yes, watch YouTube, of course. But also get yourself surrounded by people who are going to provide you a safe place to ask questions. And also give you a chance to teach what you know. I think it's the best way to solidify education that you've acquired is to start teaching it. So teach what you know. Come join us over at the I Heart AI Leadership Institute community. Love y'all. Y'all are amazing. It's super fun time to be in this space, but it's also incredibly important time in this space. We are all surrounded by people raising baby tigers. And I love baby tigers. I think they're super cute. But we need to think about what happens when those grow up. And you, if you're listening, can be part of that. You might be part of it right now. And if you are, keep going. Know that you have a friend, know that you have a community here to support you. And I am so excited to just have had a chance to chat with you all. I'm going to actually turn off this little section and I'm going to open up for some Q&A. So if you guys have some questions, I'll be happy to take them, but you'll have to chat them with me. So you have to get into the chat and like send me some messages if you'd like. That would be amazing. So signing off. Good morning, AI. Episode 2, I think. So welcome to the party. Bye. Alright friends, so what's up? I see you all. If you have questions, let me know, or if you just want to comment. Next time I do this, I will give you the opportunity to come on stage and ask your questions. So if you want, just let me know so I can— I'll send you the link. It's not for today because I didn't set it up, but, but I would love to have you all just hop on stage with me and, and have a conversation, ask a question, even provide an answer. Like I said, I hope you found it interesting and exciting today. And if I, if I— oh, I see. Yes, great. Awesome. So one of the questions that I, that I was asked, and I'm only unfortunately looking at my LinkedIn chat right now, but one of the questions that I get asked all the time and just came in was, you know, if I'm only going to do one thing, What would I do? And so I created a guide. It's free. It's just a guide, but I created a guide. And if you send me an email, actually, and I'm going to give you my email address. It's a cool email. I'm actually testing something out. So maybe you guys could do this with me. I'm going to see if I could type a message in here. I'm going to try and type it in, but it's just the word start, like getting started. So start at at first— oopsie— firstmile.ai. Just call me Stark. Firstmile.ai is our new campaign to help companies in their first mile. But I'd love— and I'm going to send you the response to this. I'm going to set up an autoresponder, but I will just reply to you with a URL that'll take you to this guide. And I'm going to put it in. Oh, it went to YouTube, Facebook. And Facebook. All right, cool, nice. So if you're on LinkedIn, I will, I will put it in the chat, or if somebody is listening, you guys can put it in the chat. Um, but one of the things people always wonder, like, I just saw a link on, or an article on LinkedIn, and it said 250 classes, 250 classes in AI that you could take. Like, as a, as, I mean, I'm sure they were trying to be helpful, but But what are you going to do with that? One thing I do want to remind you is that like humanity, certainly Americans, when we buy books, less than 10% of us finish the books we buy. Less than 10% of us finish the courses we start. So if you get a list of 250 courses, you might not even finish the first one. So I wanted to shrink that up. And layer in some education. So I created what's called an upskilling guide, and it's been— I mean, I, I think 14,000 people have downloaded it at this point, so that's kind of exciting. Um, but if you haven't downloaded it yet, it's a great— like, it's just a condensed version of like, here's the bare minimum you need to do. And I've now of course created an AI for Professionals workshop that I'm going to be launching in a couple weeks. Um, so you could take that. You can even come to Miami and take it with me live. What? But I can also go to you if you want me to come and teach it for your company, or you can just take the online version. So it's a really great opportunity for us. But, but I would love to, I'd love to have you guys come to Miami because that would be super fun for us to learn AI. But it's all about, it's very similar to the AI content that I released a couple weeks ago for Valentine's Day, where you really need to understand how AI is changing business.. And business is, for the most part, every business is sales, is marketing, is delivery. And so that's the chunk for professionals. That's what you need to understand. Even if you're not a seller, even if you're not a marketer, you need to understand, like, how is AI going to help you accelerate your ability to get in front of the people who are going to pay you? And now some of us are cogs in a machine, no offense. Like, some of us are PMs delivering on a project. Maybe you have a side hustle, right? Maybe you wanna be an entrepreneur. Maybe you wanna understand how do I do use AI to play with the, to play with the big dogs to become, right? Have my own company. I think this technology is going to unlock so much potential in individuals and in companies. So this is my contribution to that, to that world, to a new world where we're actually doing higher value work. And that means that we have to reinvent who we're going to be and what we're going to do. So I will, I will be around. I'm going to actually hop. I'll be on LinkedIn. Send me a message on LinkedIn. Oh, don't forget to connect with me on LinkedIn if you haven't yet. Thank you all for joining me. I had 5,100 people show up, so that's 5,100. Amazing. Like, I think it would be so cool if I could actually Now publish this. So I've done a few of these so far. I've not put anything on my podcast, but I named it. This is my podcast. So I'm going to publish this podcast hopefully today. But happy International Women's Day. Oh wait, declare it's International Women's Day. Say hi to a woman near you. Tell them that it's their day. Let them know that you care about what they say. You value their contributions and you can't wait to see more of us in every room that you're in. I hope you've had a great time today. Thanks for being with me. It is right at the top of the hour. I try to be super respectful with your time and it's been fun. I hope you liked it. We'll see you next week. Remember, every single day I will be coming to you live. Good morning AI at Microsoft's MVP Summit. So see you there. Bye everyone.