Jacqueline Rinehart: The Rise of Artificial Intelligence

Show Notes

In this episode, Jacqueline Reinhart discusses the transformative power of AI and its impact on various industries. She explains the concept of collective intelligence, which combines the strengths of humans and machines to achieve better results. Jacqueline provides examples of AI in daily life, including virtual agents, predictive analytics, and robotic technologies. She also distinguishes between traditional AI and GenAI, highlighting the differences in their design and transparency. Jacqueline offers strategies for businesses considering AI investments, emphasizing the importance of understanding the purpose, data strategy, talent requirements, and compliance. She discusses the role of AI startups in driving innovation and advises on selecting reliable vendor partners. Jacqueline shares examples of AI applications in healthcare and banking, such as disease identification, drug discovery, and personalized financial recommendations. She raises ethical concerns related to AI, including user rights, privacy, intellectual property, and environmental impact. Jacqueline concludes by encouraging society to shape the future of AI through defining guidelines and considering the broader implications.

Takeaways

·         AI combines the strengths of humans and machines to achieve better results, enhancing human intuition, creativity, and intelligence.

·         AI has transformed various aspects of daily life, including search engines, virtual agents, personalized recommendations, and robotic technologies.

·         GenAI is an emerging technology that differs from traditional AI in its design and lack of transparency, requiring careful consideration and evaluation.

·         When investing in AI, businesses should consider their goals, data strategy, talent requirements, compliance, and the potential for ROI.

·         AI startups play a crucial role in driving innovation and providing cost-effective solutions for businesses of all sizes.

·         AI is revolutionizing industries such as healthcare and banking, enabling disease identification, drug discovery, robotic surgeries, and personalized financial recommendations.

·         Ethical dilemmas in AI include defining acceptable uses, protecting user rights and privacy, honoring intellectual property, and addressing environmental impact.

Chapters

00:00  Introduction

00:31 The Transformative Power of AI

03:13 AI in Health and Science

04:07 AI in Personal Life and Retail

05:05 The Difference Between AI and GenAI

06:18 Considerations for Investing in AI

12:18 The Role of AI Startups

29:53 Ethical Dilemmas and Considerations in AI

34:57 Closing Thoughts and How to Connect

 

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Melissa Aarskaug (00:01.99)
Welcome to the Executive Connect Podcast. I'm excited to have Jacqueline Reinhart 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.

Jacqueline Rinehart (00:26.615)
Hi, thank you for having me. It's such an honor.

Melissa Aarskaug (00:31.494)
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.

Jacqueline Rinehart (00:48.545)
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, we 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 humanist, our intuition, our creativity, our innate intelligence, and then working with machines to do all the mundane tasks or high level super fast calculations.

It creates a really great 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 and 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,

Well, I 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 Mobileye Technology that senses our environment and helps us manage automobile collision avoidance, driver assistance and autonomous vehicles.

Jacqueline Rinehart (03:13.111)
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's drug research, there's discovery and trials that are moving at a pace that otherwise wouldn't have been possible without artificial intelligence. You think about robotic assisted surgeries.

Also, robots are helping us in first responder situations, going where canines and other humans cannot go to find folks in disasters. You think about Perseverance, which is a robot exploring Mars, or you think about in our own personal home, something like Roomba 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 Stitch Fix. 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 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 Bard 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, that human machine partnership is astounding and with the incredible continued innovation and vision of humans being able to design things that make our lives easier and more fun and healthier as humans is

Jacqueline Rinehart (05:34.647)
Really a great view of how AI is transforming our world and why I believe that such a transformative technology both today and for the future of its continuing growth.

Melissa Aarskaug (05:49.294)
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 as the major differences are between the two?

Jacqueline Rinehart (06:24.471)
It's really good because it's 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 that have been around for so long, and again, since the 60s, and just more widely used today, it's something that is classic. And its foundation really gives us

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, technology isn't about doing, but learning. 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 and things we need to be concerned about as a society, often with people forgetting that it's emerging, so it's just the beginning and it's gonna go through lots of iterations. But if you wanna understand just one level down as you asked about the difference between AI and GenAI, AI.

as itself is a continuation and GenAI is an evolution of it. But GenAI 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 GenAI is very rule -based. So it makes decisions on defined rules and also sometimes statistics, whatever comes out of it.

will always be the same based upon what you put into it. What's in it is knowledge that's put in by experts and it learns by using that very rule -based structure to meet 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...

Jacqueline Rinehart (08:45.527)
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 pretty much has 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, it's 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 from 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. So it takes data.

and a foundation of it. And then it teaches itself in a very self -perpetuating 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 can give you different answers with the same question. And it also creates totally new data, new text, 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.

Jacqueline Rinehart (11:07.639)
And it also has shown to have a high level of bias and manipulation and there is no accountability or ultimately no trust in it. So when again this nature of emerging technology, it's transparent to its leaders even. So if you look at a very vocal leader of OpenAI that is the owner and producer creator of ChavitgPT, their CEO will quote say the product sucks.

And the CTO will 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 buzz is that there is an incredible track record of societal and business usage success in what we have come to know and label as classic AI and GenAI is ultimately new and it's

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.

Melissa Aarskaug (12:18.438)
That's a great definition. I know there's probably not a week that goes by, Jacqueline, 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?

Jacqueline Rinehart (12:44.855)
Yes, so the idea of practicalness is sort of how I lend this. So if a technology that everything is sort of bucketed in AI versus making this distinction of classic traditional AI versus gen AI, you look at things and investment like anything else in terms of what's practical, what's reasonable, and as you mentioned ROI. So I'm looking at it.

considering using AI, my direction would of course be, you know, directionally to do go with something's proven. But before you get to that sort of conclusion of proven or unproven like a GenAI 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 both an AI and an innovator combined, you can do a lot of great transform and

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 costs 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 I am doing this. I think this is a good investment. Maybe your business, you know, I want to make sure I at least get 1 % return or I want to break even, right? That's 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 question and the line of questioning that I'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 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.

Jacqueline Rinehart (15:08.727)
And data ultimately doesn't sound very sexy to people. So the data that you want to use, put 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 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 asked as an investor and a business executive in this space, unlike some other things, it's really important in this 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 is structured, comes together, and ultimately delivers on that result.

and also successfully shows in our.

Now, when you're doing this too, aside from those basic questions, similar to anything that has to do with technology, particularly with data and 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, given your industry or industries, it might be really critical not only to get the...

Jacqueline Rinehart (17:07.049)
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 foundationally your data is clean and clear. But the data strategy of how it moves, 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 and 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, but could be cool to say that you are actually doing a Gen .E .I. project.

Melissa Aarskaug (18:25.574)
Yeah, it made me think when you were talking about all that, like everybody's an 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?

Jacqueline Rinehart (18:42.871)
Well, what's interesting is with a startup, 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, you know, when I think about how these startups are really helping in this space is that, you know, 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, domain 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, 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, that opens up the space of potential efficiencies and benefits that you can get to AI that make that bottom line ROI happen in addition to whatever it is you want while you're doing it. But also, when something isn't in your...

space and control. Well, 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 a viable product that it's been around and it is supportive, 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 in newer startups, they're looking to get their clients and for you to be their test for it. So that could, you know, 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

Jacqueline Rinehart (21:01.111)
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 too, when you go with these third partner vendors, that they do have those relationships, but what's, they do have that track record of success, but what's also something they have are relationships.

So they have relationships with 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 hype vendor. And I think people 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 vendor partnership path, they may not realize how important third party assessments are, and their compatibility, and their 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 in their due diligence right before implementation or when they've invested a lot in the relationship and spent time and money is that when you go through these third -party assessments to consider who is 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.

Jacqueline Rinehart (23:21.815)
Along the way or opening yourself to additional risks are really important in selecting the right vendor.

Melissa Aarskaug (23:29.51)
Yeah, I think I love the stories I'm hearing about all the different industries that are using AI very differently. 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?

Jacqueline Rinehart (23:50.327)
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 in partnership with diabetic.

retinopathy. So those capabilities and the nuances of what a machine can do and the layering and the nuance it can discover in 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, you know, gazillions amounts of data to be able to scale that drug discovery process, doing the same as well to repurpose drugs, or even provide patients 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 in 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 robotic assisted surgeries. They can use the data piece of looking through all the preop medical records to then if a surgeon is 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.

Jacqueline Rinehart (26:00.087)
And then, you know, the virtual health assistance and chatbots that I think a lot of us are getting used to 24 -7, you know, asking questions, getting answers, helping us with our medical management, reminding us to take medicine. So there's a lot of stuff going on in health care. From a banking perspective and helping people, there are things that have been going on around chatbots as well, asking and answering questions.

You know, there is, people forgot these things that had been in the headlines and we have been using for many years, things around robo advising. There are things around, you know, complimentary to things where there are markets and 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 banking for people of all different kinds of socioeconomic and demographic backgrounds. And then 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 assistants, is one of the areas where, besides healthcare and some of the banking and robotics, marketing has been...

great, great in using things because it's content generated, it's ad and optimization, because particularly when you look at the emerging technology of Gen .ai, this is 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 .ai.

marketing capabilities if you are just really wanting to do something in Gen .ai 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 .ai.

Melissa Aarskaug (28:11.334)
Yeah, you mentioned banking. I think I had 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 status. They listened 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 kinds of suggestions of ways to change my relationship with any organization.

Jacqueline Rinehart (28:47.543)
Yeah. 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 certain hours and you had to squeeze in, whether it was old, 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, taking care of your kids, on vacation, you can have access to this information that makes your financial investment and personal security a lot more easily managed.

Melissa Aarskaug (29:27.654)
Yeah, absolutely, I would agree. In kind of closing, just a couple 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 about your kind of closing thoughts on that space.

Jacqueline Rinehart (29:53.719)
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 its potential to exponentially grow and 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 I see as 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 malintended 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 AI 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 are our user rights so that we could be collectively a part of it. Because it will not like the model that is in the nature of GenA. I and how society is evolving is ultimately these organizations, these companies are out to make money.

but they also are things that we rely upon to live, to have a better quality of life. So that blending of business versus personal starts to create a crack in, 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...

Jacqueline Rinehart (32:09.927)
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, in 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, whatever form that honoring of intellectual property rights that I own it should be acknowledged. And if the usage of what I created in society wants to be used in gen AI, then there should be a structure that honors that. And that compensates me for that as well. What's also interesting.

which I think it's a little noise, but not a lot, is also the environmental impact, the computing power that is 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 we define what AI can do and what 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 GPT -5? 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?

Melissa Aarskaug (34:17.158)
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 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.

Jacqueline Rinehart (34:57.367)
Sure. So I think the three big things to take away from the state of AI is, number one is there's an incredibly established track record of success 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 GenAI emerging technology.

which is completely undefined. It's sort of like the wild west of back in the day, we needed to define rules and structures and things that as a society, we can contribute to shape it because it's ultimately gonna impact us. And then 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, there's gen AI.

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, be engaged. I guess, you know, to your point, if anybody has any questions, you know, feel free to find me on LinkedIn. Just my name, Jacqueline Reinhardt.

I'm based in New York, so I think there are a lot of Jacqueline Reinhart's 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 a buzzy words, because at the end of the day, it's not that complicated.

And the complicated nuances of it, 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.

Melissa Aarskaug (37:09.606)
Thank you for that. Thank you so much for being here today on the Executive Connect podcast.