Jacqueline Rinehart: The Rise of Artificial Intelligence

Should we all be burning up the planet to play around on chat GPT-5? No, or think about Apple Siri or Amazon's Alexa, these virtual agents. They are in our lives, interwoven in what we do, and we rely on them heavily, and they are part of our daily or frequent interactions in the world. Welcome to the Executive Connect podcast. I'm excited to have Jacqueline Reinhardt with us here today to talk about navigating the AI revolution.

Jacqueline was previously an executive of Bank of Hawaii. She is an AI revolutionary, harnessing the power of tech, data, and people. Welcome Jacqueline. Thank you for having me at such an honor. We're excited to have you today. And jumping right in as I normally do, talk to me about how AI is currently transforming our world, and why you believe it is such a transformative technology for our future. Absolutely. So when you think about AI, it actually has been around since the 1950s.

And for all of us, sort of in our everyday lives, it started using it and adopting it at the turn of the century. And the reason why it's so transformative is it takes what I call this concept of collective intelligence, which is the best of humans, the best of machines, and you put them together and you get a better result than each of them individually.

And so when you think about that kind of potential, humans working, doing things that enhance our human needs, our intuition, our creativity, our innate intelligence, and then working with the machines to do all the mundane tasks or high-level super fast calculations, it creates a really great society of a partnership. And this framing of collective intelligence is really how AI has been framed and continues to be framed.

So the grim movie reality of robots taking over the world in humans is definitely not how it's intended, nor is how it has been designed. So when you think about how it's transforming our world, while it used a bunch of words, I wanted to use some images for us to make it real how AI has really just changed our daily usage, behaviors, and interactions. So you think about something like Google. Well, that's a machine search, and that's AI.

Think about Apple Siri or Amazon's Alexa, these virtual agents, they are in our lives, interwoven in what we do, and we rely on them heavily, and they are part of our daily or frequent interactions in the world.

You think about Netflix using those predictive analytics to make suggestions for us and customizing our views, or a company called Mobile Eye Technology, that senses our environment, and helps us manage around automobile collision avoidance, driver assistance, and autonomous vehicles. So again, when you think about how it's transforming, you see, wow, all these things are happening, and that's just in our general lives.

You add in things like in the health and science space, there is drug research, there's discovery and trials that are moving in a pace that otherwise wouldn't have been built. And possible without artificial intelligence. You think about robotic assisted surgeries. Also, robots are helping us in first responder situations, going where canine and other humans cannot go to find ropes and disasters.

You think about perseverance, which is a robot exploring Mars, or you think about it in our own personal home, something like Rumba, and a robotic vacuum cleaner that goes around cleaning, right? Fun things like that, or things like artificial intelligence, categorizing photos on Airbnb, or helping deliver personalized clothing recommendations to our door with a company like StitchFix.

I mean, so many things when you think about the transformation, it's not just words, it's about all the things around us, and all the possibilities of how it's changed our lives in what's now being called the fourth industrial revolution. And then there's emerging gen A technology that made a splash in November 2022 with the release of chat GPT.

So you, so you look at chat GPT and it takes machine search to a whole other level than Google search has done. It also gives us a more storytelling edge to our searches. Then you also have growing and similar text products like Farad or Gemini, or image products like Dolly, where you say, "Please give me an image like this," and it creates it, or Sora, that does the same thing, but creates video. So when you think about how it's impacted our lives, the human machine partnership is astounding.

And with the incredible continued innovation and vision of humans, being able design things that make our lives easier and more fun, and healthier as humans is really a great view of how AI is transforming our world, and why I believe it's such a transformative technology both today and for the future of its continuing growth. Absolutely. I personally love the convenience of AI. Like you mentioned, with several of the businesses that you mentioned, I use myself.

I love the convenience of, you know, looking for a movie and there's recommendations for other movies I've watched, or getting my clothes delivered or my food delivered. And it's been around for a while, right? Some of this is not new technology. So when we look at generative AI and AI, what do you see the major differences are between the two?

That's really good because a good question, and that's because as we as a society start talking about all these technologies, there's not often a really clean language to talk about it, and things that I'll call AI, and I have been around for so long, and again, since the 60s and just more widely used today, it's something that's classic. And it's foundation really gives us a technology framework, an architecture that allows us to support models of thinking, perception, and action.

So it does all these kinds of things and pulls it all together to the experiences that we get to have with that technology. Also the technology isn't about doing, but it's it learns. Now, while gen AI is a continuation of this concept, it's very different, and I feel that difference is what gets a lot of the headlines.

So if you think about it and things we need to be concerned about as a society, often with people forgetting that it's emerging, so it's just beginning and it's going to go through lots of iterations. But if you want to understand just one level down as you asked about the difference between AI and gen AI, AI as itself is a continuation and gen AI is an evolution of it, but gen AI takes a different direction.

So in terms of how it's structured, so AI itself up until the introduction of what we see now as gen AI is very robust. So it makes decisions on defined rules and also sometimes statistics, whatever sums out of it will always be the same based upon what you put into it. The what's in it is knowledge that's put in by experts, and it learns by using that very rule-based structure to wheat rules and to add new rules.

So it's a very controlled technology in terms of what happens behind the hood and managing it. It's transparent, you know where the data is coming from, how it's selected, how it's used, how it's trained, and it's auditable, so you can audit it. It has low to medium levels of bias, and it's pretty much a high level of accountability and trust.

And again, because it's rule-based and it's also been evolving and developing over 60 plus years and has a quarter century of practical society and business usage, it's a very classic traditional framework for artificial intelligence. Now with gen AI is completely different actually, and so while it's an artificial intelligence, it's something new and it's emerging. And when you use in technology the word emerging, it means it's new, it may not be reliable.

And given its design is very different than what up until this point we've become used to and relied upon as a mature AI technology, it's not. So gen AI in terms of how it's designed, unlike traditional AI which is very rule-based, very controlled, it isn't, but it takes data and a foundation of it. And then it teaches itself and there is self-perpecculating way and learns itself. So it takes new information and new data, learns new patterns and continues to mimic and evolve by itself.

And if you ask it a question, it gives you, it can give you different answers with the same question. And it also creates totally new data, new tax, new video. So given its dynamics and its emerging, when you see things in the headlines about it and the leaders about it, it's because this new design and its new nature doesn't give it a lot of transparency or trust.

So unlike what we may call classic or traditional AI, it's unclear where gen AI gets its data, how it selects it, or even if it's accurate. And it also has shown to have a five level of bias, manipulation, and there is no accountability or ultimately no trust in it. So when, again, this needs to have emerging technology, it's transparent to its leaders even.

So if you look at a very vocal leader of open AI, that is the owner and producer creator of Chapatchee PT, there's C-C-O-Quote, say the product sucks. And it's C-T-O-We'll say that she has no idea where the data is coming from when she's asked about video data. So again, it's really important in understanding this landscape and all the bias, is that there is an incredible track record of societal and business usage success

in what we have come to know, or label as classic AI. And gen AI is ultimately new. And it's almost, we have to just wait and see, and have fun with it, while it evolves into something that's a bit more stable. Transparent, reliable, and trustworthy. That's a great definition. I know there's probably not a week that goes by, Jaclyn, where people aren't asking me,

should I invest in AI? Should I not? Should I allow it? From your perspective, what are some strategies that businesses can consider when deciding on whether to invest in AI technology, and really looking to get an ROI out of whatever their investment is? Yeah, so the idea of practicalness is sort of how

I lens this. So if a technology that everything is sort of bucket in AI versus making this distinction of classic traditional AI versus gen AI, you look at things in an investment, like anything else, in terms of what's practical, what's reasonable, and as you mentioned ROI. So I'm looking at considering using AI, my direction would of course be, you know, directionally to go with something's proven, but before you get to that sort of conclusion of proven or unproven like a gen AI product,

you really should ask the basic questions of why are you doing this? Why do you feel you need to invest in AI? Because interestingly as a both an AI and an innovator combined, you can do a lot of great transformative work, not always investing in technology. So think about why are you doing something? And then why are you doing it to your point? Think about the investment cost and what you're expecting to get a return on your investment if you want to go this route.

So if you say to yourself, I know why I'm doing this, I think this is a good investment, maybe your business, you know, I want to make sure I at least get one percent return or I want to break even, write that something to consider. Then once you understand why and are clear why you're doing it, then you go to the next step and you dive deeper, what do I want to solve for? And in the context of

big easy wins. So this questioning and the line of questioning that'll follow is pretty much the same logic and structure that you would use for any business decision making in today's modern data and technological world. But it's just a new product, a new technology, nothing really to get

to question your ability in using your same discernment to make those choices. So once you know what you want to solve for and have an idea of what those big easy wins are, you want to know what data you actually want to use for this endeavor because AI while it sounds super sexy, the foundation of it is data and data ultimately doesn't sound very sexy to people. So the data that you want to use putting into it and what you want to get out of it is really clear. Now why is it important to talk

about this data thing because you have to have a data strategy around it, right? For AI to effectively work, the data has to be clean and it has to be valid. That foundation has to make sure that everything is in place for you then to go and put it into something that then transforms it,

learns from it and gives you outputs. Now aside from the basics of questions you'd ask as an investor and a business executive in the space, unlike some other things, it's really important in the space to have the right talent to introduce, manage and maintain anything that you're doing from an AI

perspective. The reason being is the nuances in supporting the right investment choices, the products, managing the risks and really getting the ROI requires somebody who's been doing this for many years and really can be that expert advisor to help orchestrate and make ensure that everything a structure comes together and ultimately delivers on that result and also successfully

shows an ROI. Now when you're doing this to aside from those basic questions, similar to anything that has to do with technology, particularly with data in AI, also you have to make sure that there's transparency in what you're doing for accountability, traceability and to make sure that you can audit it. Also giving your industry or industries, it might be really critical not only to

get the privacy and the security components in place but also the compliance framework. So when you think about data as being key to artificial data, your data strategy has to make sure foundation layer data is clean and clear. But the data strategy of how it gets stored, is it protected? Are you complying with regulatory guidelines around data and other management? Is it secure? All of these things are really important in considering the investment because it's

not just, I'm going to invest in this and that's the end of it. There's a whole co-investment part in the operations, the governance and the management of this. Once you go through all these questions, as part of your thinking, you also have to think about, am I going to do this myself? Am I going to go in-house? Or am I going to go with a third-party partner or vendor? And then finally, do you want to go

with something that's proven, things that have been around for years and years? Or do you want to go with something that's unproven? That's also risky and may not give you the return, any return on your investment. Could be cool to say that you are actually doing a GenNI project. Yeah, made me think when you were talking about all that, like, everybody's in AI now. Everybody is doing AI or there's a bunch of new AI startups that I hear all the time. How do you think these new

startups are changing the industry right now? Well, what's interesting is with the startups, so there's different kinds of startups, there's startups that are super new, and then there are startups that

have been around for several years. And so, when I think about how these startups are really helping in this space is that looking at things from a practical perspective, unless you have a massive budget, an internal talent and an internal talent team, that includes data scientists, domains, subject matter engineers, data engineers, all kinds of folks, and being able to build and maintain a product like this, going to these AI startups as a third party partner or vendor partner is a really

great cost-effective model that allows companies of all kinds of sizes to adopt and integrate AI. That being said, so that opens up the space of potential efficiencies and benefits that you can get to AI that make it make that bottom line ROI happen in addition to whatever it is you want it while you're doing it. But also, when something isn't in your space and control, while third party vendors are really important in this space, there are some things that you need to be careful about

that you may not necessarily have to deal with if you were doing it on your own. So when you go through these third party assessments, it's really important to make sure that they actually have viable product that it's been around and it is supportable and has evolved because often in the innovation space, whether it's AI or otherwise, the product doesn't really work so great. They're looking particularly newer startups, they're looking at their clients and for you to be

their test for it. So that could, depending on your choices and how you want to do it, picking the right partner is key. And it's important to realize too if you look at things from a classic AI traditional space, there are lots of companies who actually have a solid track record

of success for years and years and are necessarily new to the market. Also, in this space, it's really important to you when you go with the third partner vendors that they do have those relationships, they do have that track record of success, but what's also something they have are relationships.

So they have relationships with other stack, other vendors and partners that create a coexisting tech stack that allow for each of these vendors to work together in an interoperable way and to enable a lot more fluidity and full capability results when you go with a more established type vendor.

And I think people, they miss that and I know that goes beyond your question, but just saying, what value do they add and just saying, oh, it gives you this great, a great result and a more cost effective one, there's more to it because often when people go down the vendor management path and their partnership path, they may not realize how important third party assessments are and their compatibility and they're fitting with other tech stacks when you're going through a lot

of innovation and transformation in the world. And then another piece that I want to talk about, and it's just something that I always feel people forget and they learn this and they do diligence right before implementation or when they've invested a lot in their relationship and spent time and money is that when you go through these third party assessments to consider who's going to be your vendor, you also have to make sure that they also are compliant with data privacy governance

laws in the regions you operate and also where they store their data in regions because sometimes some of those things may not be compliant with your region or even the data storage issues may not be compliant with your corporate policies. So in a very proactive way, the AI space is and the startup space allows for such incredible opportunities, but navigating in a way that avoids any hiccups along the way or opens yourself to additional risk or really important and

selecting that right vendor. Yeah, I think I love the stories I'm hearing about all the different industries that are using AI very different. I know you gave a little bit at the beginning, but when I think of other verticals like healthcare and banking, can you share some examples of how some of your clients are using AI today in different sectors? Yeah, so there are all kinds of

incredible things that people are using it for. So when you look at healthcare as an example, there is disease identification, often in imaging that can be overlooked by the human eye. So an example would be Google Healthcare did something on partnership with diabetic

retinopathy. So those capabilities in the nuances of what a machine can do and the layering and the nuance thing it can discover is and partnership with humans is an incredible breakthrough or you think about the mass amounts of data that go into a drug design and bringing it to market.

So you have machines amidst these the drug discovery creation process that can go through gazillions amounts of data to be able to scale that drug discovery process, doing the same as well to be purpose drugs or even provide a patient with genetic information and also within the context of all the medical information, tailor medical treatments as well and an even better way for patients. There are things that an healthcare around robusted robot assisted surgeries. So they assist

surgeons during operations. They also with some of their teeny tiny gadgets provide enhanced precision, stability, and control and also what I find this piece of it always found I found

really interesting as it can combine all kinds of the robotic assisted surgeries. They can use the data piece of looking through all the pre-opt medical records to then if surgeon is going through some going through a part of our body with a little teeny tiny instrument that there could be some sort of adjustment in real time based upon the processing as well of all these data medical records.

So it's just it's incredible. And then you know the virtual health assistance and chat pots that I think a lot of us are getting used to 24/7 asking questions, getting answers, helping us with our medical management, reminding us to take medicine. There's a lot of stuff going on in healthcare from a banking perspective and helping people. There are things that have been going on around

chat pots as well asking and answering questions. You know there is people forget these things that had been in the headlines we have been using for many years things around a robot advising.

There are things around you know complementary to things where there are markets and new new startups in the financial services space that go beyond traditional lending models and they're doing it through all kinds of beautiful machine learning to open the marketplace for for baking for people of all different kinds of socioeconomic and demographic backgrounds. And then another another area which I know you didn't touch on but I'll briefly mention is marketing.

So marketing also similar to chatbots and virtual assistance. This is one of the areas where aside from healthcare and some of the banking and the robotics, marketing has been great great in using things because it's content generated is ad and optimization because particularly when you

look at the emerging technology of Gen A.I. This was one area of marketing where if you were thinking about investment beyond current AI tools it's a low risk kind of fun enhancement to add in some of these Gen A.I. marketing capabilities if you were just really wanting to do something in Gen A.I but with low risk. Marketing is the place to enhance something that you're doing. And just to say and have some fun with Gen A.I.

Yeah, I you mentioned banking. I think at a recent opportunity to work with an AI tool at the bank where they made suggestions I never even thought about Jacqueline. I'm like this is brilliant and I didn't need to authenticate or validate or share my social day. Listen to my voice and it was my password and they were able to make recommendations on switching my account from this to that which I appreciate those kind of suggestions of ways to change with my relationship with an any organization.

And what's and what's really nice about banking too is that in all these spaces but banking in particular is banking used to be bound to start at hours and you had to squeeze in whether it was all the old school going to the bank or trying to call someone to get some additional information

or getting advice from somebody bound to that sort of work day. So if you're busy working, you can care of your kids, you know, on vacation, you can 24/7 have access to this information that makes your financial investment and personal security a lot more easily managed. Yeah, absolutely, I would agree. It kind of closing just a couple of other thoughts as we push

the boundaries of AI and technology, do you see any ethical dilemmas that we should be aware of or any biases or anything that we should be considering like intellectual property? Just curious on your kind of closing thoughts on that space. I do. I think as a society overall technology may have had similar concerns that we'll talk about but with the introduction of Gen AI and it's potential to exponentially grow and it

exponentially be adopted. I think at the start of its emergence phase, it's really important for all of us to have voices to drive some solutions around some of what ICS ethical dilemmas. One of them is a simple question of, you know, given its emerging nature, Gen AI, how do we define what AI can and

cannot be used for? The potential of it getting access to so much information, doing so many things, there should be some guidelines because as we've seen as a society this century, the opportunity for rogue and now-intended individuals, governments as well, we really need to as a society really establish that baseline question of, here's what AI can be used for and this is absolutely what it

cannot be used for. In that framework of then what can it be used for, I think it's important because of the ubiquitous nature of data and information for each person in society to have a definition of what our user rights so that we could be collectively a part of it because it will, like the model that this in the nature of Gen AI and how society is evolving is ultimately in these organizations, these

companies are out to make money but they also are things that we rely upon to live, to have better quality of life so that blending of business versus personal starts to create a crack and well,

how do we navigate that? This is a business space ultimately and we need to start treating it a bit with some more ethical and end users in mind and also establish accountability so what are those user rights that all of us should have in managing our data and our information and within that what privacy concerns need to be addressed if everything under the sun about me is accessible through all these models and permutations and addition to my rights if I'm not aware of something

at what point is my privacy being protected and then also you know you mentioned intellectual property rights if I create something in the world whatever industry you would ever form that honoring of intellectual property rights that I own it should be acknowledged and if the usage of what I create in society wants to be used in Gen AI then there should be a structure that honor that

and that compensates me for that as well. What's also interesting which I think gets a little noise but not a lot is also the environmental impact the computing power that has required to manage and compute some of these things if we're scaling this at such a large degree it will ultimately contribute to negatively impact our environment at a very severe scale and so I think it loops back into the question all of these back into how do we define what AI can do and what can

it cannot be used for. So as a silly example a light example should we all be burning up the planet to play around on chat GBT5 or should there be guidelines around modeling from a broader perspective both about the economics the people and the environmental elements when we're defining that model of AI and that framework as a society of how we want it to look like going forward.

Yeah that's fantastic I would agree with the environmental piece and you're right it doesn't get a lot of discussion or talking but I'm sure as we move into this further it will start to be a major concern for the environment. Just a couple one last question for you so much good information

thank you so much for sharing just in closing thoughts for our listener maybe three top things to consider in AI and where we are today and then kind of the second piece of that sharing a little bit about you how we can connect with you and get in touch with you. Sure so I think the three big three big things to take away from the state of AI is number one is there's an incredibly established track record of success in this century in terms of what AI can do for us in a way

that protects us that's more secure and transparent. The second is there's a Genai emerging technology which is completely undefined is sort of like the wild west of back in the day we need to define rules and structures and things that as a society we can contribute to shape it because it's ultimately going to impact us and then three is don't get overwhelmed with all this stuff everything you've heard really the beginning part of this talk it's really basic there's AI and there's Genai

and all those smart capabilities and rationalities and reasonings and things that we know how to do on a daily basis as leaders and contributors to society the same rules apply it's just a new subject matter so know that being engaged I guess you know to your point if anybody has any questions you'll feel free I to find me on LinkedIn just my name Jacqueline Reinhardt I'm based in New York so that I think there's not a lot of Jacqueline Reinhardts in the US but that's me and I also blog

about this a bunch on LinkedIn so I try to highlight things and whether it's ethics whether it's some basics and foundational stuff whether it's things that have touched on some of the things we've talked about today and I do I try to make it really simple and not too busy words because at the end of the day it's not that complicated and the complicated nuances of it are right that those are people in the world who have those kinds of jobs and we can navigate all those technical folks as

business investors and as business leaders to shape it in the same framework as we've been doing in the past thank you for that thank you so much for being here today on the Executive Connect Podcast yeah and thank you again Steve for having me and for all the great interesting subjects that you have folks talk about that can keep us really engaged in forms and have a little fun in our day as well thank you thank you [BLANK_AUDIO]