1
00:00:00,000 --> 00:00:04,760
Should we all be burning up the planet to play around on chat GPT-5?

2
00:00:04,760 --> 00:00:11,240
No, or think about Apple Siri or Amazon's Alexa, these virtual agents.

3
00:00:11,240 --> 00:00:18,840
They are in our lives, interwoven in what we do, and we rely on them heavily,

4
00:00:18,840 --> 00:00:22,960
and they are part of our daily or frequent interactions in the world.

5
00:00:26,560 --> 00:00:30,400
Welcome to the Executive Connect podcast.

6
00:00:30,400 --> 00:00:38,040
I'm excited to have Jacqueline Reinhardt with us here today to talk about navigating the AI revolution.

7
00:00:38,040 --> 00:00:42,640
Jacqueline was previously an executive of Bank of Hawaii.

8
00:00:42,640 --> 00:00:48,720
She is an AI revolutionary, harnessing the power of tech, data, and people.

9
00:00:48,720 --> 00:00:51,040
Welcome Jacqueline.

10
00:00:51,040 --> 00:00:58,280
Thank you for having me at such an honor. We're excited to have you today.

11
00:00:58,280 --> 00:01:06,240
And jumping right in as I normally do, talk to me about how AI is currently transforming our world,

12
00:01:06,240 --> 00:01:13,080
and why you believe it is such a transformative technology for our future.

13
00:01:13,080 --> 00:01:14,080
Absolutely.

14
00:01:14,080 --> 00:01:22,320
So when you think about AI, it actually has been around since the 1950s.

15
00:01:22,320 --> 00:01:32,440
And for all of us, sort of in our everyday lives, it started using it and adopting it at the turn of the century.

16
00:01:32,440 --> 00:01:41,920
And the reason why it's so transformative is it takes what I call this concept of collective intelligence,

17
00:01:41,920 --> 00:01:50,560
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.

18
00:01:50,560 --> 00:02:00,640
And so when you think about that kind of potential, humans working, doing things that enhance our human needs, our intuition,

19
00:02:00,640 --> 00:02:11,120
our creativity, our innate intelligence, and then working with the machines to do all the mundane tasks or high-level super fast calculations,

20
00:02:11,120 --> 00:02:17,200
it creates a really great society of a partnership.

21
00:02:17,200 --> 00:02:24,680
And this framing of collective intelligence is really how AI has been framed and continues to be framed.

22
00:02:24,680 --> 00:02:32,000
So the grim movie reality of robots taking over the world in humans is definitely not how it's intended,

23
00:02:32,000 --> 00:02:36,000
nor is how it has been designed.

24
00:02:36,000 --> 00:02:52,160
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.

25
00:02:52,160 --> 00:02:55,200
So you think about something like Google.

26
00:02:55,200 --> 00:02:59,200
Well, that's a machine search, and that's AI.

27
00:02:59,200 --> 00:03:16,640
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.

28
00:03:16,640 --> 00:03:27,120
You think about Netflix using those predictive analytics to make suggestions for us and customizing our views, or a company called Mobile Eye Technology,

29
00:03:27,120 --> 00:03:37,600
that senses our environment, and helps us manage around automobile collision avoidance, driver assistance, and autonomous vehicles.

30
00:03:37,600 --> 00:03:46,080
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.

31
00:03:46,080 --> 00:03:57,040
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.

32
00:03:57,040 --> 00:04:00,000
And possible without artificial intelligence.

33
00:04:00,000 --> 00:04:03,120
You think about robotic assisted surgeries.

34
00:04:03,120 --> 00:04:15,440
Also, robots are helping us in first responder situations, going where canine and other humans cannot go to find ropes and disasters.

35
00:04:15,440 --> 00:04:23,720
You think about perseverance, which is a robot exploring Mars, or you think about it in our own personal home, something like Rumba,

36
00:04:23,720 --> 00:04:27,640
and a robotic vacuum cleaner that goes around cleaning, right?

37
00:04:27,640 --> 00:04:43,160
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.

38
00:04:43,160 --> 00:04:50,440
I mean, so many things when you think about the transformation, it's not just words, it's about all the things around us,

39
00:04:50,440 --> 00:04:58,840
and all the possibilities of how it's changed our lives in what's now being called the fourth industrial revolution.

40
00:04:58,840 --> 00:05:09,800
And then there's emerging gen A technology that made a splash in November 2022 with the release of chat GPT.

41
00:05:09,800 --> 00:05:19,400
So you, so you look at chat GPT and it takes machine search to a whole other level than Google search has done.

42
00:05:19,400 --> 00:05:25,080
It also gives us a more storytelling edge to our searches.

43
00:05:25,080 --> 00:05:35,880
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,

44
00:05:35,880 --> 00:05:39,320
or Sora, that does the same thing, but creates video.

45
00:05:39,320 --> 00:05:46,760
So when you think about how it's impacted our lives, the human machine partnership is astounding.

46
00:05:46,760 --> 00:05:57,000
And with the incredible continued innovation and vision of humans, being able design things that make our lives easier and more fun,

47
00:05:57,000 --> 00:06:13,640
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.

48
00:06:13,640 --> 00:06:17,880
Absolutely. I personally love the convenience of AI.

49
00:06:17,880 --> 00:06:23,000
Like you mentioned, with several of the businesses that you mentioned, I use myself.

50
00:06:23,000 --> 00:06:30,680
I love the convenience of, you know, looking for a movie and there's recommendations for other movies I've watched,

51
00:06:30,680 --> 00:06:33,560
or getting my clothes delivered or my food delivered.

52
00:06:33,560 --> 00:06:36,440
And it's been around for a while, right?

53
00:06:36,440 --> 00:06:38,760
Some of this is not new technology.

54
00:06:38,760 --> 00:06:47,000
So when we look at generative AI and AI, what do you see the major differences are between the two?

55
00:06:47,000 --> 00:06:57,560
That's really good because a good question, and that's because as we as a society start talking about all these technologies,

56
00:06:57,560 --> 00:07:07,560
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,

57
00:07:08,600 --> 00:07:14,280
and again, since the 60s and just more widely used today, it's something that's classic.

58
00:07:14,280 --> 00:07:25,080
And it's foundation really gives us a technology framework, an architecture that allows us to support models of thinking,

59
00:07:25,080 --> 00:07:29,000
perception, and action.

60
00:07:29,000 --> 00:07:35,800
So it does all these kinds of things and pulls it all together to the experiences that we get to have with that technology.

61
00:07:36,280 --> 00:07:40,840
Also the technology isn't about doing, but it's it learns.

62
00:07:40,840 --> 00:07:52,520
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.

63
00:07:52,520 --> 00:08:00,280
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,

64
00:08:00,280 --> 00:08:03,720
so it's just beginning and it's going to go through lots of iterations.

65
00:08:04,280 --> 00:08:11,400
But if you want to understand just one level down as you asked about the difference between AI and gen AI,

66
00:08:11,400 --> 00:08:20,520
AI as itself is a continuation and gen AI is an evolution of it, but gen AI takes a different direction.

67
00:08:20,520 --> 00:08:30,520
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.

68
00:08:31,160 --> 00:08:45,480
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.

69
00:08:45,480 --> 00:08:59,160
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.

70
00:08:59,160 --> 00:09:06,600
So it's a very controlled technology in terms of what happens behind the hood and managing it.

71
00:09:06,600 --> 00:09:17,800
It's transparent, you know where the data is coming from, how it's selected, how it's used, how it's trained,

72
00:09:17,800 --> 00:09:22,040
and it's auditable, so you can audit it.

73
00:09:22,600 --> 00:09:30,520
It has low to medium levels of bias, and it's pretty much a high level of accountability and trust.

74
00:09:30,520 --> 00:09:43,560
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,

75
00:09:43,560 --> 00:09:52,520
it's a very classic traditional framework for artificial intelligence.

76
00:09:53,240 --> 00:10:03,880
Now with gen AI is completely different actually, and so while it's an artificial intelligence, it's something new and it's emerging.

77
00:10:03,880 --> 00:10:11,400
And when you use in technology the word emerging, it means it's new, it may not be reliable.

78
00:10:11,400 --> 00:10:22,200
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,

79
00:10:22,760 --> 00:10:35,480
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.

80
00:10:35,480 --> 00:10:41,160
And then it teaches itself and there is self-perpecculating way and learns itself.

81
00:10:41,160 --> 00:10:49,320
So it takes new information and new data, learns new patterns and continues to mimic and evolve by itself.

82
00:10:49,800 --> 00:10:57,080
And if you ask it a question, it gives you, it can give you different answers with the same question.

83
00:10:57,080 --> 00:11:03,480
And it also creates totally new data, new tax, new video.

84
00:11:03,480 --> 00:11:12,360
So given its dynamics and its emerging, when you see things in the headlines about it and the leaders about it,

85
00:11:12,520 --> 00:11:19,960
it's because this new design and its new nature doesn't give it a lot of transparency or trust.

86
00:11:19,960 --> 00:11:31,640
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.

87
00:11:31,640 --> 00:11:41,080
And it also has shown to have a five level of bias, manipulation, and there is no accountability or ultimately no trust in it.

88
00:11:41,800 --> 00:11:48,120
So when, again, this needs to have emerging technology, it's transparent to its leaders even.

89
00:11:48,120 --> 00:11:57,640
So if you look at a very vocal leader of open AI, that is the owner and producer creator of Chapatchee PT,

90
00:11:57,640 --> 00:12:01,640
there's C-C-O-Quote, say the product sucks.

91
00:12:01,640 --> 00:12:07,480
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

92
00:12:08,360 --> 00:12:15,880
video data. So again, it's really important in understanding this landscape and all the bias,

93
00:12:15,880 --> 00:12:21,320
is that there is an incredible track record of societal and business usage success

94
00:12:21,320 --> 00:12:28,520
in what we have come to know, or label as classic AI. And gen AI is ultimately new.

95
00:12:28,520 --> 00:12:38,280
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.

96
00:12:38,680 --> 00:12:41,240
Transparent, reliable, and trustworthy.

97
00:12:41,240 --> 00:12:49,480
That's a great definition. I know there's probably not a week that goes by, Jaclyn, where people aren't asking me,

98
00:12:49,480 --> 00:12:57,640
should I invest in AI? Should I not? Should I allow it? From your perspective, what are some strategies that

99
00:12:57,640 --> 00:13:05,480
businesses can consider when deciding on whether to invest in AI technology, and really looking to get

100
00:13:05,480 --> 00:13:15,160
an ROI out of whatever their investment is? Yeah, so the idea of practicalness is sort of how

101
00:13:15,160 --> 00:13:23,000
I lens this. So if a technology that everything is sort of bucket in AI versus making this distinction

102
00:13:23,000 --> 00:13:29,480
of classic traditional AI versus gen AI, you look at things in an investment, like anything else,

103
00:13:29,480 --> 00:13:37,400
in terms of what's practical, what's reasonable, and as you mentioned ROI. So I'm looking at

104
00:13:37,400 --> 00:13:45,960
considering using AI, my direction would of course be, you know, directionally to go with something's proven,

105
00:13:45,960 --> 00:13:51,480
but before you get to that sort of conclusion of proven or unproven like a gen AI product,

106
00:13:51,480 --> 00:13:56,920
you really should ask the basic questions of why are you doing this? Why do you feel

107
00:13:57,640 --> 00:14:04,920
you need to invest in AI? Because interestingly as a both an AI and an innovator combined,

108
00:14:04,920 --> 00:14:10,680
you can do a lot of great transformative work, not always investing in technology. So think about

109
00:14:10,680 --> 00:14:15,800
why are you doing something? And then why are you doing it to your point? Think about the investment

110
00:14:15,800 --> 00:14:21,960
cost and what you're expecting to get a return on your investment if you want to go this route.

111
00:14:21,960 --> 00:14:28,280
So if you say to yourself, I know why I'm doing this, I think this is a good investment, maybe your business,

112
00:14:28,280 --> 00:14:33,080
you know, I want to make sure I at least get one percent return or I want to break even,

113
00:14:33,080 --> 00:14:39,400
write that something to consider. Then once you understand why and are clear why you're doing it,

114
00:14:39,400 --> 00:14:45,640
then you go to the next step and you dive deeper, what do I want to solve for? And in the context of

115
00:14:45,640 --> 00:14:51,240
big easy wins. So this questioning and the line of questioning that'll follow is pretty much

116
00:14:51,240 --> 00:15:00,120
the same logic and structure that you would use for any business decision making in today's modern

117
00:15:00,120 --> 00:15:07,240
data and technological world. But it's just a new product, a new technology, nothing really to get

118
00:15:07,240 --> 00:15:14,840
to question your ability in using your same discernment to make those choices. So once you know

119
00:15:14,840 --> 00:15:21,640
what you want to solve for and have an idea of what those big easy wins are, you want to know what data

120
00:15:21,640 --> 00:15:31,640
you actually want to use for this endeavor because AI while it sounds super sexy, the foundation

121
00:15:31,640 --> 00:15:39,480
of it is data and data ultimately doesn't sound very sexy to people. So the data that you want to use

122
00:15:39,480 --> 00:15:46,760
putting into it and what you want to get out of it is really clear. Now why is it important to talk

123
00:15:46,760 --> 00:15:52,440
about this data thing because you have to have a data strategy around it, right? For AI to effectively

124
00:15:52,440 --> 00:15:59,320
work, the data has to be clean and it has to be valid. That foundation has to make sure that

125
00:15:59,320 --> 00:16:05,800
everything is in place for you then to go and put it into something that then transforms it,

126
00:16:05,800 --> 00:16:15,240
learns from it and gives you outputs. Now aside from the basics of questions you'd ask as an investor

127
00:16:15,240 --> 00:16:22,440
and a business executive in the space, unlike some other things, it's really important in the space

128
00:16:22,440 --> 00:16:28,360
to have the right talent to introduce, manage and maintain anything that you're doing from an AI

129
00:16:28,360 --> 00:16:37,880
perspective. The reason being is the nuances in supporting the right investment choices, the

130
00:16:37,880 --> 00:16:44,760
products, managing the risks and really getting the ROI requires somebody who's been doing this

131
00:16:44,760 --> 00:16:52,760
for many years and really can be that expert advisor to help orchestrate and make ensure that

132
00:16:52,760 --> 00:17:01,080
everything a structure comes together and ultimately delivers on that result and also successfully

133
00:17:01,080 --> 00:17:08,840
shows an ROI. Now when you're doing this to aside from those basic questions, similar to

134
00:17:08,840 --> 00:17:16,920
anything that has to do with technology, particularly with data in AI, also you have to make sure

135
00:17:16,920 --> 00:17:23,320
that there's transparency in what you're doing for accountability, traceability and to make sure

136
00:17:23,320 --> 00:17:31,000
that you can audit it. Also giving your industry or industries, it might be really critical not only to

137
00:17:31,000 --> 00:17:36,760
get the privacy and the security components in place but also the compliance framework. So when

138
00:17:36,760 --> 00:17:42,760
you think about data as being key to artificial data, your data strategy has to make sure

139
00:17:42,760 --> 00:17:49,320
foundation layer data is clean and clear. But the data strategy of how it gets stored, is it

140
00:17:49,320 --> 00:17:58,360
protected? Are you complying with regulatory guidelines around data and other management?

141
00:17:58,360 --> 00:18:04,120
Is it secure? All of these things are really important in considering the investment because it's

142
00:18:04,120 --> 00:18:11,080
not just, I'm going to invest in this and that's the end of it. There's a whole co-investment part

143
00:18:11,080 --> 00:18:17,240
in the operations, the governance and the management of this. Once you go through all these questions,

144
00:18:17,240 --> 00:18:23,000
as part of your thinking, you also have to think about, am I going to do this myself? Am I going to go

145
00:18:23,000 --> 00:18:30,520
in-house? Or am I going to go with a third-party partner or vendor? And then finally, do you want to go

146
00:18:30,520 --> 00:18:35,720
with something that's proven, things that have been around for years and years? Or do you want to

147
00:18:35,720 --> 00:18:42,280
go with something that's unproven? That's also risky and may not give you the return, any return

148
00:18:42,280 --> 00:18:47,880
on your investment. Could be cool to say that you are actually doing a GenNI project.

149
00:18:47,880 --> 00:18:55,960
Yeah, made me think when you were talking about all that, like, everybody's in AI now. Everybody is

150
00:18:55,960 --> 00:19:03,240
doing AI or there's a bunch of new AI startups that I hear all the time. How do you think these new

151
00:19:03,240 --> 00:19:10,840
startups are changing the industry right now? Well, what's interesting is with the startups, so there's

152
00:19:10,840 --> 00:19:15,320
different kinds of startups, there's startups that are super new, and then there are startups that

153
00:19:15,320 --> 00:19:24,120
have been around for several years. And so, when I think about how these startups are really helping

154
00:19:24,120 --> 00:19:30,520
in this space is that looking at things from a practical perspective, unless you have a massive

155
00:19:30,520 --> 00:19:37,480
budget, an internal talent and an internal talent team, that includes data scientists, domains,

156
00:19:37,480 --> 00:19:45,800
subject matter engineers, data engineers, all kinds of folks, and being able to build and maintain

157
00:19:45,800 --> 00:19:53,560
a product like this, going to these AI startups as a third party partner or vendor partner is a really

158
00:19:53,560 --> 00:20:02,200
great cost-effective model that allows companies of all kinds of sizes to adopt and integrate AI.

159
00:20:02,200 --> 00:20:14,200
That being said, so that opens up the space of potential efficiencies and benefits that you can get

160
00:20:14,200 --> 00:20:20,600
to AI that make it make that bottom line ROI happen in addition to whatever it is you want it while

161
00:20:20,600 --> 00:20:33,720
you're doing it. But also, when something isn't in your space and control, while third party vendors

162
00:20:33,720 --> 00:20:38,840
are really important in this space, there are some things that you need to be careful about

163
00:20:38,840 --> 00:20:46,520
that you may not necessarily have to deal with if you were doing it on your own. So when you go

164
00:20:46,520 --> 00:20:51,960
through these third party assessments, it's really important to make sure that they actually have

165
00:20:51,960 --> 00:20:59,880
viable product that it's been around and it is supportable and has evolved because often in

166
00:20:59,880 --> 00:21:06,040
the innovation space, whether it's AI or otherwise, the product doesn't really work so great.

167
00:21:06,040 --> 00:21:12,360
They're looking particularly newer startups, they're looking at their clients and for you to be

168
00:21:12,360 --> 00:21:18,440
their test for it. So that could, depending on your choices and how you want to do it,

169
00:21:18,440 --> 00:21:24,840
picking the right partner is key. And it's important to realize too if you look at things from

170
00:21:24,840 --> 00:21:31,640
a classic AI traditional space, there are lots of companies who actually have a solid track record

171
00:21:31,640 --> 00:21:40,280
of success for years and years and are necessarily new to the market. Also, in this space, it's really

172
00:21:40,280 --> 00:21:46,280
important to you when you go with the third partner vendors that they do have those relationships,

173
00:21:46,280 --> 00:21:53,880
they do have that track record of success, but what's also something they have are relationships.

174
00:21:53,880 --> 00:22:02,120
So they have relationships with other stack, other vendors and partners that create a coexisting

175
00:22:02,120 --> 00:22:09,320
tech stack that allow for each of these vendors to work together in an interoperable way and to enable

176
00:22:09,320 --> 00:22:18,680
a lot more fluidity and full capability results when you go with a more established type vendor.

177
00:22:18,680 --> 00:22:23,000
And I think people, they miss that and I know that goes beyond your question, but just saying,

178
00:22:23,000 --> 00:22:28,920
what value do they add and just saying, oh, it gives you this great, a great result and a

179
00:22:28,920 --> 00:22:36,440
more cost effective one, there's more to it because often when people go down the vendor management

180
00:22:36,440 --> 00:22:42,760
path and their partnership path, they may not realize how important third party assessments are

181
00:22:42,760 --> 00:22:48,680
and their compatibility and they're fitting with other tech stacks when you're going through a lot

182
00:22:48,680 --> 00:22:55,000
of innovation and transformation in the world. And then another piece that I want to talk about,

183
00:22:55,000 --> 00:23:00,920
and it's just something that I always feel people forget and they learn this and they do diligence

184
00:23:00,920 --> 00:23:07,080
right before implementation or when they've invested a lot in their relationship and spent time

185
00:23:07,080 --> 00:23:13,400
and money is that when you go through these third party assessments to consider who's going to be

186
00:23:13,400 --> 00:23:18,840
your vendor, you also have to make sure that they also are compliant with data privacy governance

187
00:23:18,840 --> 00:23:24,680
laws in the regions you operate and also where they store their data in regions because sometimes

188
00:23:24,680 --> 00:23:30,680
some of those things may not be compliant with your region or even the data storage issues may

189
00:23:30,680 --> 00:23:37,240
not be compliant with your corporate policies. So in a very proactive way, the AI space is

190
00:23:37,240 --> 00:23:43,720
and the startup space allows for such incredible opportunities, but navigating in a way that

191
00:23:43,720 --> 00:23:50,120
avoids any hiccups along the way or opens yourself to additional risk or really important and

192
00:23:50,120 --> 00:23:57,640
selecting that right vendor. Yeah, I think I love the stories I'm hearing about all the different

193
00:23:57,640 --> 00:24:02,920
industries that are using AI very different. I know you gave a little bit at the beginning, but

194
00:24:02,920 --> 00:24:09,800
when I think of other verticals like healthcare and banking, can you share some examples of how

195
00:24:09,800 --> 00:24:16,360
some of your clients are using AI today in different sectors? Yeah, so there are all kinds of

196
00:24:16,360 --> 00:24:24,200
incredible things that people are using it for. So when you look at healthcare as an example,

197
00:24:24,200 --> 00:24:31,480
there is disease identification, often in imaging that can be overlooked by the human eye.

198
00:24:31,480 --> 00:24:37,400
So an example would be Google Healthcare did something on partnership with diabetic

199
00:24:37,400 --> 00:24:43,720
retinopathy. So those capabilities in the nuances of what a machine can do and the layering

200
00:24:43,720 --> 00:24:50,760
and the nuance thing it can discover is and partnership with humans is an incredible breakthrough

201
00:24:50,760 --> 00:25:01,160
or you think about the mass amounts of data that go into a drug design and bringing it to market.

202
00:25:01,160 --> 00:25:10,040
So you have machines amidst these the drug discovery creation process that can go through

203
00:25:10,040 --> 00:25:18,680
gazillions amounts of data to be able to scale that drug discovery process, doing the same as well

204
00:25:18,680 --> 00:25:27,800
to be purpose drugs or even provide a patient with genetic information and also within the context of

205
00:25:27,800 --> 00:25:33,560
all the medical information, tailor medical treatments as well and an even better way for patients.

206
00:25:33,560 --> 00:25:40,840
There are things that an healthcare around robusted robot assisted surgeries. So they assist

207
00:25:40,840 --> 00:25:47,800
surgeons during operations. They also with some of their teeny tiny gadgets provide enhanced

208
00:25:47,800 --> 00:25:56,440
precision, stability, and control and also what I find this piece of it always found I found

209
00:25:56,440 --> 00:26:03,080
really interesting as it can combine all kinds of the robotic assisted surgeries. They can use

210
00:26:03,080 --> 00:26:09,000
the data piece of looking through all the pre-opt medical records to then if surgeon is going through

211
00:26:09,000 --> 00:26:14,360
some going through a part of our body with a little teeny tiny instrument that there could be

212
00:26:14,360 --> 00:26:20,920
some sort of adjustment in real time based upon the processing as well of all these data medical records.

213
00:26:20,920 --> 00:26:27,800
So it's just it's incredible. And then you know the virtual health assistance and chat pots that I

214
00:26:27,800 --> 00:26:35,880
think a lot of us are getting used to 24/7 asking questions, getting answers, helping us with

215
00:26:35,880 --> 00:26:41,400
our medical management, reminding us to take medicine. There's a lot of stuff going on in healthcare

216
00:26:42,120 --> 00:26:49,240
from a banking perspective and helping people. There are things that have been going on around

217
00:26:49,240 --> 00:26:57,720
chat pots as well asking and answering questions. You know there is people forget these things that

218
00:26:57,720 --> 00:27:05,240
had been in the headlines we have been using for many years things around a robot advising.

219
00:27:05,240 --> 00:27:11,880
There are things around you know complementary to things where there are markets and new

220
00:27:11,880 --> 00:27:17,960
new startups in the financial services space that go beyond traditional lending models and they're

221
00:27:17,960 --> 00:27:26,200
doing it through all kinds of beautiful machine learning to open the marketplace for for baking

222
00:27:26,200 --> 00:27:30,840
for people of all different kinds of socioeconomic and demographic backgrounds.

223
00:27:31,800 --> 00:27:37,880
And then another another area which I know you didn't touch on but I'll briefly mention is marketing.

224
00:27:37,880 --> 00:27:44,520
So marketing also similar to chatbots and virtual assistance. This is one of the areas where

225
00:27:44,520 --> 00:27:51,960
aside from healthcare and some of the banking and the robotics, marketing has been great great in

226
00:27:51,960 --> 00:28:00,280
using things because it's content generated is ad and optimization because particularly when you

227
00:28:00,280 --> 00:28:06,600
look at the emerging technology of Gen A.I. This was one area of marketing where if you were thinking

228
00:28:06,600 --> 00:28:17,560
about investment beyond current AI tools it's a low risk kind of fun enhancement to add in some of

229
00:28:17,560 --> 00:28:24,040
these Gen A.I. marketing capabilities if you were just really wanting to do something in Gen A.I but

230
00:28:24,040 --> 00:28:30,040
with low risk. Marketing is the place to enhance something that you're doing. And just to say

231
00:28:30,040 --> 00:28:33,640
and have some fun with Gen A.I.

232
00:28:33,640 --> 00:28:43,400
Yeah, I you mentioned banking. I think at a recent opportunity to work with an AI tool at the bank

233
00:28:43,400 --> 00:28:48,840
where they made suggestions I never even thought about Jacqueline. I'm like this is brilliant and

234
00:28:48,840 --> 00:28:56,280
I didn't need to authenticate or validate or share my social day. Listen to my voice and it was my

235
00:28:56,280 --> 00:29:02,760
password and they were able to make recommendations on switching my account from this to that which I

236
00:29:02,760 --> 00:29:12,280
appreciate those kind of suggestions of ways to change with my relationship with an any organization.

237
00:29:12,280 --> 00:29:19,480
And what's and what's really nice about banking too is that in all these spaces but banking in

238
00:29:19,480 --> 00:29:25,880
particular is banking used to be bound to start at hours and you had to squeeze in whether it was

239
00:29:25,880 --> 00:29:30,680
all the old school going to the bank or trying to call someone to get some additional information

240
00:29:30,680 --> 00:29:37,400
or getting advice from somebody bound to that sort of work day. So if you're busy working,

241
00:29:37,400 --> 00:29:45,080
you can care of your kids, you know, on vacation, you can 24/7 have access to this information that

242
00:29:45,080 --> 00:29:51,160
makes your financial investment and personal security a lot more easily managed.

243
00:29:52,120 --> 00:29:59,160
Yeah, absolutely, I would agree. It kind of closing just a couple of other thoughts as we push

244
00:29:59,160 --> 00:30:06,840
the boundaries of AI and technology, do you see any ethical dilemmas that we should be aware of

245
00:30:06,840 --> 00:30:13,640
or any biases or anything that we should be considering like intellectual property?

246
00:30:13,640 --> 00:30:17,800
Just curious on your kind of closing thoughts on that space.

247
00:30:18,760 --> 00:30:27,480
I do. I think as a society overall technology may have had similar concerns that we'll talk about

248
00:30:27,480 --> 00:30:36,760
but with the introduction of Gen AI and it's potential to exponentially grow and it

249
00:30:36,760 --> 00:30:44,040
exponentially be adopted. I think at the start of its emergence phase, it's really important for all

250
00:30:44,040 --> 00:30:53,480
of us to have voices to drive some solutions around some of what ICS ethical dilemmas. One of them is a

251
00:30:53,480 --> 00:31:01,160
simple question of, you know, given its emerging nature, Gen AI, how do we define what AI can and

252
00:31:01,160 --> 00:31:09,160
cannot be used for? The potential of it getting access to so much information, doing so many things,

253
00:31:09,160 --> 00:31:16,440
there should be some guidelines because as we've seen as a society this century, the opportunity

254
00:31:16,440 --> 00:31:25,160
for rogue and now-intended individuals, governments as well, we really need to as a society really

255
00:31:25,160 --> 00:31:30,760
establish that baseline question of, here's what AI can be used for and this is absolutely what it

256
00:31:30,760 --> 00:31:38,520
cannot be used for. In that framework of then what can it be used for, I think it's important because

257
00:31:38,840 --> 00:31:48,520
of the ubiquitous nature of data and information for each person in society to have a definition of

258
00:31:48,520 --> 00:31:58,040
what our user rights so that we could be collectively a part of it because it will, like the model that this

259
00:31:58,040 --> 00:32:06,680
in the nature of Gen AI and how society is evolving is ultimately in these organizations, these

260
00:32:06,680 --> 00:32:13,640
companies are out to make money but they also are things that we rely upon to live, to have better

261
00:32:13,640 --> 00:32:23,720
quality of life so that blending of business versus personal starts to create a crack and well,

262
00:32:23,720 --> 00:32:31,400
how do we navigate that? This is a business space ultimately and we need to start treating it a bit

263
00:32:31,400 --> 00:32:39,240
with some more ethical and end users in mind and also establish accountability so what are those

264
00:32:39,240 --> 00:32:46,200
user rights that all of us should have in managing our data and our information and within that

265
00:32:46,200 --> 00:32:52,920
what privacy concerns need to be addressed if everything under the sun about me is accessible

266
00:32:52,920 --> 00:32:58,840
through all these models and permutations and addition to my rights if I'm not aware of something

267
00:32:58,840 --> 00:33:05,640
at what point is my privacy being protected and then also you know you mentioned intellectual

268
00:33:05,640 --> 00:33:12,600
property rights if I create something in the world whatever industry you would ever form that

269
00:33:12,600 --> 00:33:19,560
honoring of intellectual property rights that I own it should be acknowledged and if the usage of

270
00:33:19,560 --> 00:33:25,400
what I create in society wants to be used in Gen AI then there should be a structure that honor that

271
00:33:25,960 --> 00:33:33,400
and that compensates me for that as well. What's also interesting which I think gets a little noise

272
00:33:33,400 --> 00:33:42,520
but not a lot is also the environmental impact the computing power that has required to

273
00:33:42,520 --> 00:33:51,080
manage and compute some of these things if we're scaling this at such a large degree it will

274
00:33:51,080 --> 00:33:59,880
ultimately contribute to negatively impact our environment at a very severe scale and so I think it

275
00:33:59,880 --> 00:34:06,520
loops back into the question all of these back into how do we define what AI can do and what can

276
00:34:06,520 --> 00:34:14,600
it cannot be used for. So as a silly example a light example should we all be burning up the planet

277
00:34:14,600 --> 00:34:26,280
to play around on chat GBT5 or should there be guidelines around modeling from a broader perspective

278
00:34:26,280 --> 00:34:33,800
both about the economics the people and the environmental elements when we're defining that model of

279
00:34:33,800 --> 00:34:40,120
AI and that framework as a society of how we want it to look like going forward.

280
00:34:41,640 --> 00:34:47,160
Yeah that's fantastic I would agree with the environmental piece and you're right it doesn't get a lot

281
00:34:47,160 --> 00:34:54,520
of discussion or talking but I'm sure as we move into this further it will start to be a major

282
00:34:54,520 --> 00:35:01,240
concern for the environment. Just a couple one last question for you so much good information

283
00:35:01,240 --> 00:35:08,120
thank you so much for sharing just in closing thoughts for our listener maybe three top things

284
00:35:08,120 --> 00:35:15,400
to consider in AI and where we are today and then kind of the second piece of that sharing a little

285
00:35:15,400 --> 00:35:23,960
bit about you how we can connect with you and get in touch with you. Sure so I think the three big

286
00:35:23,960 --> 00:35:31,800
three big things to take away from the state of AI is number one is there's an incredibly

287
00:35:32,600 --> 00:35:40,440
established track record of success in this century in terms of what AI can do for us in a way

288
00:35:40,440 --> 00:35:47,800
that protects us that's more secure and transparent. The second is there's a Genai emerging technology

289
00:35:47,800 --> 00:35:55,960
which is completely undefined is sort of like the wild west of back in the day we need to define

290
00:35:55,960 --> 00:36:03,240
rules and structures and things that as a society we can contribute to shape it because it's

291
00:36:03,240 --> 00:36:09,480
ultimately going to impact us and then three is don't get overwhelmed with all this stuff everything

292
00:36:09,480 --> 00:36:17,000
you've heard really the beginning part of this talk it's really basic there's AI and there's Genai

293
00:36:17,000 --> 00:36:23,800
and all those smart capabilities and rationalities and reasonings and things that we know how to do

294
00:36:23,800 --> 00:36:30,200
on a daily basis as leaders and contributors to society the same rules apply it's just a new subject

295
00:36:30,200 --> 00:36:36,920
matter so know that being engaged I guess you know to your point if anybody has any questions

296
00:36:36,920 --> 00:36:45,400
you'll feel free I to find me on LinkedIn just my name Jacqueline Reinhardt I'm based in New York

297
00:36:45,400 --> 00:36:51,640
so that I think there's not a lot of Jacqueline Reinhardts in the US but that's me and I also blog

298
00:36:51,640 --> 00:36:57,640
about this a bunch on LinkedIn so I try to highlight things and whether it's ethics whether it's some

299
00:36:57,640 --> 00:37:02,920
basics and foundational stuff whether it's things that have touched on some of the things we've

300
00:37:02,920 --> 00:37:11,400
talked about today and I do I try to make it really simple and not too busy words because at the end

301
00:37:11,400 --> 00:37:17,720
of the day it's not that complicated and the complicated nuances of it are right that those are

302
00:37:17,720 --> 00:37:24,040
people in the world who have those kinds of jobs and we can navigate all those technical folks as

303
00:37:24,040 --> 00:37:31,080
business investors and as business leaders to shape it in the same framework as we've been doing

304
00:37:31,080 --> 00:37:38,360
in the past thank you for that thank you so much for being here today on the Executive Connect

305
00:37:38,360 --> 00:37:45,000
Podcast yeah and thank you again Steve for having me and for all the great interesting subjects

306
00:37:45,800 --> 00:37:51,000
that you have folks talk about that can keep us really engaged in forms and have a little fun

307
00:37:51,000 --> 00:37:53,720
in our day as well thank you thank you

308
00:37:53,720 --> 00:38:03,720
[BLANK_AUDIO]