MEDIASCAPE: Insights From Digital Changemakers

AI Evolution and Business Transformation with Industry Expert Scott Hebner

• Hosted by Joseph Itaya & Anika Jackson • Episode 49

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Get ready to explore the groundbreaking evolution of artificial intelligence with our special guest, Scott Hebner, a principal analyst at SiliconANGLE and theCUBE. Scott's journey from IBM engineering to becoming a pivotal voice in AI analysis provides a fascinating backdrop to our conversation. We navigate through the transformative tech cycles he's witnessed, from the internet and cloud computing to the rapid advancements of AI today. Scott shares his insights on how AI is reshaping industries by optimizing content and simplifying complex tasks, significantly impacting both everyday life and business operations.

Discover the revolution of generative AI and its profound impact on the business world over the past decade. Uncover the transition from predictive AI to the innovative capabilities of generative models like ChatGPT, including the rise of specialized small language models tailored for specific sectors. We discuss the potential of AI in coding and simplifying tasks, offering a glimpse into a future where specialized AI models continue to enhance various domains. The conversation further explores the shift from task-oriented assistants to goal-oriented agents, emphasizing the need to integrate causality into AI systems for better understanding of objectives and consequences.

Navigate the intricate landscape of AI regulations and data privacy with us as we examine the balance between innovation and protection. We delve into the risks associated with AI, such as IP rights and biases, and the global competition in AI development, drawing parallels to cryptocurrency debates. The episode also underscores the importance of building networks for digital success, particularly within the Master of Science in Digital Media Management program at USC. Join us as we encourage students to engage with industry leaders and continue their educational journey, expanding their knowledge through connections.

This podcast is proudly sponsored by USC Annenberg’s Master of Science in Digital Media Management (MSDMM) program. An online master’s designed to prepare practitioners to understand the evolving media landscape, make data-driven and ethical decisions, and build a more equitable future by leading diverse teams with the technical, artistic, analytical, and production skills needed to create engaging content and technologies for the global marketplace. Learn more or apply today at https://dmm.usc.edu.

Speaker 1:

Welcome to Mediascape Insights from Digital Changemakers, a speaker series and podcast brought to you by USC Annenberg's Digital Media Management Program. Join us as we unlock the secrets to success in an increasingly digital world.

Speaker 2:

Welcome to another episode of Mediascape Insights from Digital Changemakers, promoted by USC Annenberg Digital Media Management Master's Program. I am one of your co-hosts, anika Jackson, and I am here with one of my favorite, most recent AI connections Scott Hebner. He is a principal analyst for AI at SiliconANGLE and the Cube. Scott Hebner he's a principal analyst for AI at SiliconANGLE and theCUBE. Scott, you're also a board member for an advisory role for so many different startups and you also had a very lengthy career at IBM, moving up into various CMO and VP roles. So I'm really excited to have you here to kind of unveil a little bit about the history of AI. I've spoken about it a little bit on the podcast, but not going super in depth, and I know that you have some recent papers and you have a really good perspective on where it's been, where we're going, where we are and then where we're going next. So just thank you for being here.

Speaker 3:

This is thank you. This is going to be a lot of fun and I appreciate it.

Speaker 2:

These are always fun conversations for me because I love talking about where we are and getting into the nitty gritty, but then also imagining what the possibilities are. What was your career trajectory into? You know, working in tech in these various roles and then moving into AI.

Speaker 3:

Oh God, if I go all the way back. It was like, okay. So my dad was an engineer at ibm and I didn't know what I wanted to do. I just wanted to go to university of massachusetts and have fun, right, and he talked me into getting, you know, an engineering degree. So that sounds good. So I didn't realize it was gonna be you know torture, but somehow I got through it. I actually got through it because I had a friend, one of my roommates, who was just Always had a Heineken in his hand, but just really brilliant, so he was like my tutor. But by the time I finally got my degree I realized I wasn't going to be doing real live engineering work. I mean, that stuff's really complicated, as you can imagine, and I ended up going to IBM and, long story short, I think I did more talking about what the technology can do because I was able to bridge. I could sort of understand the technology at a deeper level, but I was also able to communicate it. So that got me into like product management roles and marketing.

Speaker 3:

I was the CMO of several IBM's businesses and been through three major technological transformations. I actually when I was in college believe it or not I mean we were just starting to get PCs. There was no cell phones, no internet. You know, if you wanted to look something up you had to go to the Cyclopedia Botanica, but you know. So I was able to go through many of these technology cycles. You know the internet, and then cloud computing, the internet of things, which you know we're all familiar with now.

Speaker 3:

And then, most recently, before I left IBM, was AI. So I was the chief marketing officer for IBM's AI business and then, when I ended up leaving IBM, I got into the analyst world and I've been focused over the last couple of years on just you know what's really the next frontier, where's this all heading? Because it's so much in its infancy. I mean, it's really in its infancy and I guess the good news there's bad news and good news about my being my age One is you know you're older. The good news is you get to see the same movie over and over again, but it's just different characters and slightly different plots. So I think the AI thing is following some of the previous technical, you know transformations that have occurred in society and the world, but at a much faster clip. So what you can do today versus what you could do three years ago versus what you're going to be able to do in three years from now with AI is absolutely incredible.

Speaker 2:

Yeah, you know there's so much to discuss with this topic. One thing I really try to impart on my students as we were talking before we jumped on is I remember when we started talking about generative AI a couple years ago and getting students to start using it and feel comfortable and not being afraid of using it. And now we see that in the consumer base, where brands talk about the latest technology in their TV or in their products instead of saying AI driven, because consumers are still wary. And these students are coming from various degrees of expertise or brand new to the digital space and they're trying to learn all the different tools. I'm trying to really encourage them at least start using Gen AI, start understanding what this tool looks like, and then let's go in the next class we'll talk about this other thing that you can use to apply on top of that, to just enhance, increase their knowledge of different tech stacks within the ecosystem.

Speaker 3:

Yeah, I mean I think that's the right approach is I think there's so many new capabilities coming out at such a rapid pace. But the foundation for us everyday people you know, not businesses, but like everyday people I do think is generative AI. You know, be co-pilot for Microsoft or it's. You know, chatgpt or Perplexia is the one that I really like. I use that quite a bit. It's amazing the things you can do. I mean from you know, when I write like a you know 15 page research note and you know you want to put tags in it where you publish it, you know, for search optimization, you just throw that thing in there and say give me all the best tags and you couldn't paste it. You don't have to do it by hand anymore, right? Or if you have one table of a, you know a whole bunch of like say, it's 500 different rows and columns and you have another one that was for two years later and you want to know what the change is. You just throw them both in there, say tell me what has changed in. You know what was in row C but is now not in row C two years later, and it just does it for you, right? I mean, not to mention, you know, doing research. I use it all the time.

Speaker 3:

If you just have a question like how is statistical probabilities in AI different than causality, and it just gives you a beautifully written out thing, I think the productivity just for just doing almost anything is really really incredible. Again, it's just really the start of what we're all going to be able to do. Having said that, I do think you got to be careful with what we call hallucinations and bias, and sometimes it conceals influential factors so you can't rely on it like a hundred percent, because there's still things that need to be fixed, that I think over time it'll get better and better. But I'm a huge fan of AI. I think all this talk about it taking over humans and all that stuff is overblown. It's just going to make us all smarter, more productive, make better decisions, problem solve better. You know it's just going to make life easier for everybody. I think it's going to be. The good is going to way outweigh any potential bad that comes from it.

Speaker 2:

And definitely want to go down that rabbit hole, but maybe in a little bit. One of the things that I have been using and testing this week is custom GPTs, along with voice wave technology, so that I'm able to just go into and I will say I love Claude. But for this particular use case, I have a custom GPT that somebody created and it's taking me through an exercise for a new product that I'm developing. Right, it's a course and it has these other things, and so it's taken me through the whole building the brand persona, customer personas, pain points, messaging, demographics, psychographics, what I'm actually delivering and I love the fact that I can voice memo to it, so that, instead of having to spend all the time typing which I love writing, but it's sometimes it's easier to get your thoughts out if you can just voice them. It's picking it all up, synthesizing it and then saying, okay, here are some other questions for clarification and then summarizing the key points to put into my deck. And that's, I think, a really beautiful use case for AI technology, for gen AI technology specifically.

Speaker 2:

So you know, you left IBM. You've been working with all of these different organizations. You are truly getting deep into research, analytics, thinking about what's current and what's coming next. Will you take us back to? We know that AI big AI has been around right for 50-ish years. What's happened in the last 10 years that you were mentioning before we jumped on that has really helped to get to the point where we are now.

Speaker 3:

Yeah, if we go back about 10 years, I think two things started happening in the world of AI. First of all, the data that everyone had to deal with, and particularly in businesses. I think this started a decade ago in earnest in business. So the concept of AI in academia and research and some of the very technical sciences, if you will have been there for decades, but in business it started about a year ago and I think what was really driving it was two things. One is businesses had so much data and so much of it changed all the time that there was no way humans could understand it. So there needed to be more intelligent ways to process the data and you know, analytical tools could only do so much. The second thing that started happening is companies came out with new platforms and tools that democratized the ability for everyday businesses to use AI. So I think the first phase maybe up to a couple of years ago I think the first phase maybe up to a couple years ago was what we would call predictive AI. Think of it as the underlying foundation of all this, which basically are massive statistical probability machines and they just swim in a lake of massive data and they can identify patterns and anomalies right within, you know, statistically, within these massive data lakes, and then from that they're able to predict what may happen in the future, because it learns from all those problems, right, and so it can predict or forecast something that may happen or whatever. That's how businesses started using it predicting, well, what is my financial quarter going to look like next quarter? Or you know, if there's a hurricane that comes up to East Coast, you know, what should I do with inventory? Because it would learn from past experiences and be able to give you here's what you should do. It tells you the what. So that's how businesses use it. It was kind of under the covers of everyday tools they would use. And then, as you alluded to a few minutes ago, when was it Late? 2022, I think Maybe it was 2021 when chat, gpt came out right, and that was the first generative AI tool that the masses started to see. And what that did is it took the predictive models under the covers and took it to a whole new level of what they call neural networks. So your brain is like I don't know, billions and billions of little neurons that somehow communicate to process information and think of it as what handles all your memory and then your instincts. So, like my dogs, right, you know, if I get up in the morning, I start packing. They know I'm going somewhere and they're instinctive and they get, you know, defensive, right? That's sort of how generative AI works behind the covers. It builds all this memory in what's called a large language model and then it's able to process all that memory to actually generate content. So you can have a dialogue with you now and then you can say I want to create me an image of, you know, a rabbit, you know being chased by a fox, and it will create it, right? And then you can start saying, well, can you make it more cartoonish and can you make a big sun in the background? And it will just iterate with you and you can create a nice image. Or and you gave some of the examples before right, you know, you're able to just do some incredibly amazing things.

Speaker 3:

The genre of AI that is predominant today are like from the big players, like Microsoft and OpenAI and you mentioned Claude. You know you got Google, all these players right. Think of them as one size fits all, like the massive solved world piece. They just suck up all the data. The data lake is the internet, so it's great for everyday things, right. What you're alluding to is sort of.

Speaker 3:

I think where this is starting to head to is now you're starting to get what they call small language models, which SLMs. They're not necessarily small, but think of S as being specialized. You're getting these more domain-specific, industry-vertical, profession-centric. So one of my three sons is getting a law degree down at the University of Texas and there are chat GPTs for law right, for case law and doing all that kind of stuff.

Speaker 3:

There's those for if you're running a retail shop. There's those for the you're running a retail shop. There's those for the digital media and the publishing and what you're seeing now is a whole bunch of these more specialized models that aren't as big and huge and massive, but they're really focused on what you're trying to do domain of what you're trying use, case of what you're trying to use it for, and it may feed off the mother of all you know generative stuff, but it's really built with more knowledge and more usability for what you're trying to do and you can see how that's going to progress over time. Where you're going to be, things are going to be built for very, very specific things, like potentially discovering drug. You know new, new therapies and drug therapies and you know, and that's sort of like I think what we're in now is the big one-size-fits-all are now being complemented by hundreds and hundreds, if not thousands and tens of thousands, of really small, more specialized models for like the one you mentioned.

Speaker 2:

Yeah, and I also know that AI is really good at coding. Just to think about when we're thinking about people working in digital media they may not know how to code, they might not know how to put together the backend of a website, but we're moving also to a phase where we can ask AI to create the code for us thing I would recommend everyone do is the easiest thing to do with the Gen AI stuff today is ask it any question.

Speaker 3:

Give up on your search engine, because you'll see that it's not even worth it right. Go to like ChatGPT or Copilot or whatever, and any question you have. How do I do this? You know what happened at this timeframe. You know what was the day of. You know January 24th 1942? What? You know what day of the week was it? I mean anything you want to know. You know I used it this morning.

Speaker 3:

Someone said I can do a meeting at you know four o'clock GMT time. I'm like what the hell is GMT time? That's a quick question. By the way, you can do it by voice, right? Yeah, but I think it's really good at how do you actually accomplish something Like I need to go through this and actually asking it to do it for you, and that's where you get into coding, right?

Speaker 3:

I think this is what we call in the industry AI assistants or chatbots the fact that it's built on huge amounts of memory of how people have accomplished a task in the past and therefore can work out the instincts of that. You can ask it to write code that does something and it will write it for you. That's a task right, and it's interesting because marketing and sales and coding are the two biggest use cases in business for generative AI. Right, because they're using them as assistants and it's incredible the amount of code they can write and the quality assurance of the code and running a system to make sure that they work. The productivity is like off the ceiling, you know, just off the walls. But again, what it's good for is if you have a task to be completed.

Speaker 3:

Now where we're heading from here is more than just task, and that's where I think, it's going to get interesting.

Speaker 2:

And that's what I wanted to talk about next, because you alluded to the fact that you just put out a paper about this a couple of days ago. I know that when I'm looking over my show notes, one of the big topics was going to be the next frontier of AI we're removing, which is AI agents. So can you talk a little bit about the transition from this task-oriented use case into what is next and how you're seeing it, either in action now or how you see it in the future, like will we all have our own personal agents now, or how you see it in the future, like will.

Speaker 3:

we all have our own personal agents. You know what does this look like. Yeah, so there's a lot of streams we can go down with a question like this, but let's keep it at a more impact. Where I think the impact is going to be, you know, in a more of just like you know, an everyday kind of person's world Today, like I've been alluding to, is generative.

Speaker 3:

Ai is really good at if you have a task, create this image. Summarize this 45-page research paper. I can't read it all, I won't understand it, but what is it really trying to say? You know those kind of tasks it's really incredibly effective at because, again, it works on memory, past learnings, if you will, and then is able to accomplish these tasks. Where I think this is going to head is you have agents that we call now. So you have a task is done by assistant.

Speaker 3:

So when you hear the terminology about an agent, what they're really saying is an agent is more about a goal. There's two dimensions to think about. You're going from a task to asking it to help you accomplish your goal. I'll come back to that. The second is you're going from using it to get information to using it to actually help figure out what actions to take. So think about a goal. If you're asking it to create, I want you to create me some code that will automatically publish the article, track all the places people come to read it from and then send me an email once a month. It will write the code for you. That's a task You're asking it to accomplish a task.

Speaker 3:

If you were to ask it, I want to grow my following by four times and I want to become recognized as an expert in using AI in academia and in research and stuff like that. That's a little bit more difficult because you're asking it to solve a goal Mm-hmm, mm-hmm, which. What does that mean? Well, that means it needs a reason. It needs to actually say okay. First of all, I comprehend what you're asking me. Two is I can help you plan what actually needs to be done and if you're going to accomplish your goal, you're generally going to have to make decisions.

Speaker 3:

When you make decisions, there's always consequences of your decisions and there's usually multiple different decisions you can make. So how do you know what the right decision is right and then, collectively, you know making the right decision and being able to evaluate different options and how it would change. What outcome you're trying to accomplish is still not the same thing as problem solving. So how do I take a whole bunch of decisions and decisions that potentially other people may make and then actually understand how to solve the problem? That's a whole new world. That's what's meant by being a goal-oriented.

Speaker 3:

So when you hear the word AI agent as opposed to AI assistant, that's the difference task to goal. And then sometimes you'll hear agentic AI being referenced to, and that is for more, much more complex problem solving, generally involving an organization of people like you know, teams of people, and that's when one agent is too just can't handle it by itself and network of agents that actually collaborate together. They have their own decisions, their own data sets, they work off, but they work together. It's like the wisdom of crowds or like swarm intelligence.

Speaker 3:

Like you know, if you ever watch birds fly, I mean it's they're actually acting as a swarm, or it's the collective intelligence of the group that guides the group right, and we can go, if you want, a little deeper into why today's AI cannot do that.

Speaker 2:

Yeah, I'd love to hear that and I'd love to hear some examples and use cases of the move into agents and then agentic AI.

Speaker 3:

Okay, so let's start with what and I'll try not to go too deep here, but luckily my youngest son is getting a PhD in this stuff, so I have technical support. He's getting a math PhD. So, because what's underneath the covers, by the way of all this AI is some incredibly complex and eloquent math. It's all math, right, but so the way it works today with generative AI is it's correlative statistics. In other words, it's correlating data in a massive lake of data. It identifies patterns, anomalies, associations among different data points. Think of it as statistical probabilities being applied to this huge amount of data, and those probabilities could then tell you, given what has happened in the past your memory here's what's likely to happen in the future, given whatever you're asking it. And so think of it as a big machine that knows how to generate what, the probabilities of something happening, given what's happened in the past. That's good for tasks, because tasks tend to be repetitive and if you knew how to do it in the past, it can help you do it in the future. But it works in a static environment.

Speaker 3:

When you start getting to a goal-based, then you're starting to have to deal with mechanisms of causality. In other words, everything that exists has a cause, right, and therefore that's how you figure out what the effects is. Aristotle I think that's some famous quote that says once you prove the cause, you immediately prove the effect and, conversely, nothing can exist without its causes. And you think about it. It's true, right? You do almost nothing all day long without thinking about cause and effect. I make it the cost going back before I have to do the podcast, right, which I just did, right, you're thinking cause and effect. So, just going back to the technical thing here is, if today's AI does probabilities to help you execute a task, what causality tells you is how those probabilities change when the world around you changes. That's what causality means, and we all operate in a dynamic world where conditions and people and opinions, in other words, a dynamic world, right? Yeah?

Speaker 2:

all the time.

Speaker 3:

So you need to have causality to be able to comprehend a problem or a goal, make the right decisions, because everything has cause and effect relationships. If I do this, the consequences is this effect right, which is based on statistical probabilities, and infuse into it causality right, it's called causal AI so that it can understand how to do interventions, understand counterfactuals to the current state, identify what they call confounding effects, things that may affect the outcome, but you're not even thinking about it. There's always, you know, think about humans, right? There's this thing called tacit knowledge. You do a lot every day Like I can ask you how you do something and you probably won't even be able to explain it because you just do it. It's called tacit knowledge. Are going to help humans do what is really the most difficult thing, which is plan, problem solve, read, make decisions. So what an AI agent does that's going to help you solve goals, right? A goal-based approach is it needs to have those mechanisms of causal and tacit knowledge infused into it. And if you go back to what I was saying before, today's AI is really just it's memories. It's a whole bunch of data. It's trained on data from the past, but from that it learns to say, okay, given what happened in the past, whatever you're doing now with your unique circumstances, here's what you can expect to happen in the future. But that's only good if you expect your future to be just like your past, which, in business and society and everyday lives, is not the case, and that's why you really won't hear anyone today say that generative AI can help you make decisions or problem solve or can help you. You know reason, because it really can't. It's just not designed to do so. What's starting to happen now is there's new advancements that are starting to infuse that capability into the AI models and therefore you get these agents where you can have a goal right, I am losing money in my 15 stores.

Speaker 3:

I'm not. For some reason, people have stopped coming, why? First question you're going to ask is what is the problem? It's going to say well, you don't have enough people at the store, at the cash registers, and people are waiting too long in line to get out. So that's why you're losing people. But today's gender, the eye, can tell you the what. It can tell you that. But as soon as you ask it okay, well, I'm able to hire 22 more people because I can afford it. Where would I put those people across the 25 stores to optimize my revenue and my profit? That I can't do, because that's problem solved.

Speaker 3:

And think about that's what-if scenarios and say, well, we have four here and three there and two there. That's a cause and effect, right? You're saying, okay, that's one what-if scenario. What happens if I put six there and eight there and one, two and three there? Then it would be a different outcome. So you're starting to problem solve, right?

Speaker 3:

Today's AI models aren't designed to do that, because you're changing the conditions, you're intervening in the model, you're not relying just on past learnings, but you're starting to do counterfactual reasoning, the facts being where I put people right and these models can actually tell you yeah, if you put them here, you know people get in and out of the store this much faster, you'll make this much more money, given your run rate in each store and it actually I mean that would be an example of this, right, and I think that's coming. It's really going to you know, today it's helping you be more productive in doing tasks In the future. It's really going to help you make better decisions and problem solve. I don't think it's going to ever replace you. I don't think it's capable. Maybe 100 years from now, at least, when I'm gone who knows, I'll be living on Mars and all that kind of stuff. But this is in the near future, in the years ahead.

Speaker 3:

Where. Learn generative AI now, because that's going to help you with tasks, but it's coming where it's going to really be a great little person to hang out with you to do things, and it's definitely going to be true in business. It's already. People are already building it and deploying it. But I think to your earlier comment, I do think you'll get your personal assistance. I think these things will grow where they get to know you and they're and you know right now they can get to know you and they know your calendar and they know your email and then you know you can do tasks better. But it'll get more interesting and more useful.

Speaker 2:

Yeah, and that's something I really actually welcome, because I do a lot of different things we all do. We have our personal lives, we have our professional lives, we might have our academic lives. I'm podcasting, I'm teaching, I'm working for a nonprofit, I'm a single mom. I can input all of these things and have it help me better plan my schedule or look ahead to. You need to be thinking about this, this and this, which are big deadlines or other things, so I really embrace this idea. One thing of course we're recording this the week of the presidential election.

Speaker 2:

I know that there was an executive order from Biden that he says he's ahead of on for AI. There have been different state implementations that have either happened or been vetoed. There are different you know policy groups in different states. When it comes to thinking about Gen AI and the future of AI, what do you think are some ethical ways that we can move forward when we're thinking about how we're approaching the best use cases, how we're the data that's being input and just making sure that we are being good stewards of the way that we're using this amazing technology?

Speaker 3:

Well, I think to your point, this is going to become a huge challenge I think it already is particularly in businesses, and particularly in the highly regulated businesses like financial serps, like on Wall Street, or if you're doing pharmaceutical kind of stuff. Some industries aren't regulated as much and they have to report to the government. Last I saw there were 189 countries across the world that are coming out with AI-related regulations and unfortunately they're like all different, right, amy? Then, like you alluded to, in the United States, there's different approaches against different states also, and so some are going to be more restrictive, some are going to be more open, if you will, for innovation, because there's always that trade-off between stifling innovation versus protecting people, right, think is data privacy, like people's actual privacy, and you know that's a toughie, because if ai is going to start figuring stuff out and you know you want to protect people's privacy, there's also the into the intellectual property. You know you can take someone, you know, a famous entertainer, a singer, and you can basically create a new song with their mannerisms, their video, their voice, and you can create like a winning song. But do you really have IP rights to that song? Because all you did was you basically took some of the essence of someone else. Is that intellectual property? I mean there's a lot to that right.

Speaker 3:

And then you get into like the, you know, the, the, the, you know, let's say, the bad people like I. Actually, my father-in-law actually got a call from my son. What he thought was my one of my sons saying I need four thousand dollars or something like that, and he found it suspicious. And you know, you can rip off people's voices, then make phone calls or whatever, and you think it's actually, I think, if you're, you know some of the elderly like my, you know, before my mom passed, I mean I worried about that kind of stuff because you see how the scam kind of works on phishing and email and text messages. And imagine you start getting a call from someone you think you know and they're asking you to do something right. So I think there's all that protection of the criminal, the criminal element of all this. So there's a lot to unpack when we think about regulations and laws and stuff like that.

Speaker 3:

But you know, by the way, I alluded to this earlier there are inherent problems in these models in that they can they call it hallucinations. It basically said you know, super confidently, here's the answer, but it's like completely wrong. You know it goes back to the technical stuff here, which is correlation. Correlating two events or two behaviors doesn't necessarily mean causation. Correlation doesn't always mean causation right, and that is a big source of a lot of the errors or inaccuracy. And then, depending on who trained the model and what data they use, you can get biases. There can be biases around demographics of people, there can be biases in business, and so the more they open up these models and the more it trains on your data and your stuff and what you're doing, the more less biased there'll be in your world, like if you're talking about the personal assistants.

Speaker 3:

But then that gets you, you know, circles you around into the data privacy problems. So there's a whole idea. And this is what makes me wonder. When you know, without getting on one side or the other in politics, but you kind of watch a lot of these people in the Senate and the House of Representatives and you sit there and say to yourself are they really able to understand this enough to? They need to bring in a lot of data scientists and industry people that kind of really understand this before you start making these regulations because you don't want to. You know a lot of the competing countries, like China and others. I mean, you know you want to protect and you have to protect and this country, it's just you have to and you should, but at the same time you don't want to go too far than restrict innovation and have us fall behind. It's going to have huge economic impact and you know it's kind of like the whole debate around crypto.

Speaker 3:

You know, should we go crypto more aggressively? We're holding back right now, but a lot of other countries are and you know you can fall behind economically. You can become a big problem. It's the same problem here, right? So yeah that's just no matter what profession you're in ai help you make more money, because they need smart people to figure this all out, I think.

Speaker 2:

Yeah, and there's. I was just reading an article yesterday about how Meta uses they train their AI tool on all of our data, except for Brazil and Europe, because of the privacy restrictions and data privacy around the GDPR and Brazilian privacy regulations. Gdpr and Brazilian privacy regulations. So it's that concept of if you are not paying something, if you're not paying a fee for subscription, you are the product. So we think about these social media platforms and how people, or even YouTube you know different channels and how you're using that data. So then you think who's on these platforms, whether it's X or yeah.

Speaker 2:

This could go along a whole other half hour hour plus of a discussion, which we will absolutely save for another time, but these are all things that I think about when I'm thinking about data privacy and how are we good stewards? So I think everybody who's listening just needs to think about these issues, do some research, check what your privacy settings are, you know, and think about what you're feeding into the model. If you're feeding it into an open model versus, you might work for an organization that has a closed model. That's fantastic.

Speaker 3:

That's good advice, because you know, at least in my life, thousands and thousands of these terms and conditions, things that pop up and I just say, okay, never read it, Yep, we all do, we all do.

Speaker 3:

Some of these, depending on what you're doing and what you're dealing with, some of them are probably worth reading, or at least trying to. But remember, you don't have to read it now. All you've got to do is cut and paste it into a generative AI thing and say and just tell what is the essence of this. So summarize this for me. What do I need to know? You know what's going to impact my. You know, ask it whatever you want, right? How many misspellings do they have? You know you can. You can ask it a million different things.

Speaker 2:

You don't even have to really read it anymore, you just have to cut and paste it, right? You've just given me a new exercise to give next time I teach 510 in the DMM program and we talk about and we look at websites and cookies and websites in different countries and different industries. So thank you for that. I love that. I'm like okay.

Speaker 3:

You know, I think the more you experiment with it I mean, I've learned this since just over the last three or four months, since I've been doing a lot of writing and research I mean I'm finding things every day that I didn't know what can do, and I must use these two generative AI tools that I use. I must use these two genitive AI tools that I use Perplexity and ChatGPT, I don't know 20, 30 times a day, literally, but I think a big picture here is. There's and I mentioned the same movie just over and over again but different characters. I mean, even if you go back to things like this may sound stupid, but I'll say it anyhow Like when we all first started in cars great, you can, you can get to one place to another. You can, you know cars are great, but they're still dangerous to them. Right, you can get in an accident, you can hurt somebody else. There's, you know, and it's you got to go into this cautiously and how you use it. What you believe it is telling you.

Speaker 3:

Don't assume everything is a hundred percent accurate, but it's definitely better than not having it. Just like having the internet is better than not having it. Like I have this debate with my three kids all the time because they're unbelievably smart. I mean, one of them can tell me like you name any country you know. He can tell you the capital of every country in the world, for countries I've never even heard of, ever, and so you know they luckily, you know they used it over the years as they were growing up, for good.

Speaker 3:

I mean it became like, just you know, maybe they're just really smart, but I think a lot of it is like I would be. I always tell them I could be as smart as you guys if I grew up on the internet, but I didn't. I had to go to Encyclopedia Botanica or whatever. I think AI is the same thing. Right, it's good, but you have to be cautious with it and just realize it's not perfect and there are flaws and there's a lot of things that can tell you that you know are either biased or wrong or concealing influential factors, right.

Speaker 3:

Right accomplish, not because it does it on purpose, just because all it's doing is correlating patterns and behaviors and events right, and it can miss something. So whole causality and correlation aren't equal, you know. The other way of looking at this too, Anika, is that it's going to become a way of life, like the internet and your phone. So even from that angle, it's time to start really playing around with this stuff, Because when you get in the business world you'll be using it. You know you're using it. More and more professors are using it. You know, if you're in academia, researchers, I mean. It's going to become just an everyday tool of life, like I think the Internet has become.

Speaker 2:

Yeah, absolutely, and you have a great research newsletter, the YouTube channel for the Cube and also what you write on LinkedIn as ways that people can really learn more and get deep dives into what you've been sharing today.

Speaker 3:

Yeah, what I'm trying to do is it goes back to the first minute of our conversation today right, I play a technical engineer, but I'm not one. My name is it's beyond my pay grade but what I do try to do is understand what is all happening, what real businesses are doing, what some of the innovations that are coming and try to translate them into, like I kind of joke you know the mere mortals of the world, which is 99.9 of percent of us, right, you don't need to be like a deep, you know analytical ai engineer with three phds just trying to make it like here's how it's going to impact the world, how it's going to impact business. So, yeah, follow me on LinkedIn and I try to put out, you know, some research in this area and I'll be talking a lot about the agents and goals and problem solving and causality and those you know. The other thing AI doesn't do really well today either is explain to you. They're black boxes, they just go off and they do all their statistical stuff and then they just tell you the answer.

Speaker 3:

But you know we're going back to regulations. How do you do regulatory compliance when it doesn't tell you how and why it recommended or forecasted something that you just used in a real business. Now I got to explain to the government, to the regulatory process, how to do it. So there's a lot of that's. Another thing that will be coming in time is these models are going to be able to explain themselves much better and you'll be able to intervene with them and basically have a conversation with them. It's all coming To me. The stuff's not necessarily, but it's inevitable. It's going to happen Now. Will it happen next year, three years from now, five? I mean that's an open question, but the technology is there and we're going to get there, because we're barely falling now with the potential of this.

Speaker 2:

Amazing.

Speaker 3:

Yeah, it really is.

Speaker 2:

Yeah Well, thank you for sharing some of your insights today, Scott. To be continued, we'll make sure to put your information in the show notes that everybody can follow you on LinkedIn, subscribe to the newsletter and get more information, or perhaps reach out to you if they have a business case that they need to solve and need your assistance. So is there one last thing you'd like to leave everybody listening with today?

Speaker 3:

Yeah, when you just said about reach out, you know, take that to heart. You know we're all busy, but we do the best to get back. And as I look back over a 30-year career, I would say one of the most important things to do is build a network of people and try to stay in touch the best you can. It's kind of like exercise, right, you know you should exercise. Some people do it less, some do it more, but it's just like good, healthy approach to life. And I think in the world of you know, work and business and you know, build a network out and you know, and it's easier now with things like LinkedIn, right, try to stay in touch with people.

Speaker 3:

And, because it's amazing, since I left IBM and I went on the job search, nine out of 10 people I reached back out to in my network they were, you know, almost everyone's willing to help you out and, generally speaking, the ones that don't are just, you know they're too busy, right, which is understandable. Some people are busy and you know, at different times and all that. But, yeah, I would, you know, learn from others. You know your insights and your ability to be successful in life in some ways are only as good as your ability to learn from others and, unfortunately, someone like me. I learned that way later in life. I used to be, you know, I wasn't the best listener in the first half of my life.

Speaker 3:

I don't think you know, kind of like I know the answer, and it's just not true for anyone you know and second part of my life so far, I've learned to listen and like welcome all these comments and feedback, and like I would love to hear from people like did this even make sense to them? Like am I archaic as well, or you know whatever? So, yeah, that would be. My last thought is keep build a network, stay in touch. Yeah, well, and that's a lot of what we're about at usc and in this.

Speaker 2:

So yeah, that would be my last thought is keep building network, fantastic. Yeah Well, and that's a lot of what we're about at USC and in this program is building those networks, whether it's somebody who's been a guest on the podcast, somebody who's a guest speaker in class, or just with each other and with your professors, if you're listening, we love to help you out, we love to connect you to our networks. This is how we grow, this is how we learn. I love that. I get to learn from experts such as yourself. Every time we have a conversation and we'll have more conversations Some will be recorded and some won't, and every time I'll walk away with something else I want to study and learn, and that's another little piece that I can add to my knowledge base. So thank you for that, scott.

Speaker 3:

You bet, this has been fun. Yeah, it has been.

Speaker 2:

Thank you to everybody who is watching or listening to this episode of Mediascape Insights from Digital Changemakers. I'll be back again, or my co-host, Joseph Attia, will be back with another amazing guest to share some insights on your digital journey.

Speaker 1:

To learn more about the Master of Science in Digital Media Management program, visit us on the web at dmmuscedu.

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Anika Jackson

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Joseph Itaya

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